論文 - 池原　雅章

Efficient and Effective Blind JPEG Image Improvement With Sequential Feature Processing

Ezumi S., Ikehara M.

IEEE Access 12   151975 - 151986 2024年10月

研究論文（学術雑誌）, 査読有り

　概要を見る

Image compression technologies such as JPEG enable efficient use of digital images. However, these technologies also degrade the quality of the image, resulting in a poor visual appearance and reduced usability. One way to solve the quality problem and achieve high efficiency in using high-quality JPEG images is to use quality improvement technologies based on artifact removal. In recent years, various methods using machine-learning-optimized neural networks have been devised to achieve high performance whereas maintaining versatility for JPEG images with various qualities. On the other hand, methods that achieve high-quality images need enormous computing costs, which is a barrier to easy use, especially when using images with large size. Given this situation, this paper proposes a method that achieves higher performance with smaller computational resources while maintaining the image quality in broader situations. The proposed method consists of a Quality Estimation Part (QE Part) that estimates the quality of the input image and Image Processing Parts (IP Parts) that process the image based on the estimated quality representation. Sequential processing is carried out by connecting multiple IP Parts in a cascade, which enables efficient and effective processing of different features. Each IP Part consists of multiple Processing Blocks, which enable effective quality improvement while maintaining efficiency. These measures enable the proposed method to achieve state-of-the-art quantitative results and better qualitative results that outperform conventional methods for images with various qualities. The code and the pre-trained models are released at https://github.com/ezumi-keio/Sequential_Processing-main.

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Edge-Guided Low-Light Image Enhancement Based on GAN with Effective Modules

Matsui T., Ikehara M.

European Signal Processing Conference    456 - 460 2024年09月

研究論文（国際会議プロシーディングス）, 査読有り,  ISSN  22195491

　概要を見る

Under low-light conditions, the images taken may not be satisfactorily bright and may degrade in appearance. Low-light image enhancement (LLIE) is a process that converts such dim images into images taken under normal lighting conditions. The primary objectives of LLIE are to diminish noise and artifacts, maintain the integrity of edges and textures, and restore the image’s natural brightness and colors. Deep learning-based methods have shown remarkable success in this field recently but are hindered by their lengthy processing times due to intricate network architectures. To address the balance between performance and processing speed, we introduce a streamlined network equipped with efficient modules. Our approach incorporates a GAN (Generative Adversarial Network) framework enhanced with preprocessing for edge and texture extraction. We also integrate Channel Attention for color and illumination correction, Res FFT-ReLU for noise reduction, and Pixel Shuffler for high-frequency detail preservation. Our experiments show that our method surpasses traditional LLIE techniques in both quality and processing speed.

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MSARNet: Efficient JPEG Artifact Removal Using Multi-Stage Style Network

Ezumi S., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2024年01月

研究論文（国際会議プロシーディングス）, 査読有り,  ISSN  0747668X

　概要を見る

With the rapid development of photography and information processing technologies, we use more and more digital images in our daily lives. JPEG is one of the most widely used digital image formats because of its high efficiency and widespread support. JPEG Artifact Removal is a task that removes artifacts in JPEG images, such as noise and color distortion in JPEG images. Existing methods for JPEG Artifact Removal require high computational costs, which means stricter performance requirements or longer processing time. We propose a novel method for JPEG Artifact Removal named Multi-Stage style Artifacts Removal Net (MSARNet), which meets high performance, high versatility, and low computational cost. MSARNet adopts multi-stage processing, and images are processed in processing stages step by step. This process enables our proposed method to deal with different types of images effectively and efficiently. Additionally, MSARNet estimates the quality value of the input image, which contributes to effective feature processing. Experimental results show that our proposed method handles various artifacts well and outperforms existing methods in artifact removal tasks with lower computational costs.

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Multiple Adverse Weather Removal Using Masked-Based Pre-Training and Dual-Pooling Adaptive Convolution

Yamashita S., Ikehara M.

IEEE Access 12   58057 - 58066 2024年

研究論文（学術雑誌）

　概要を見る

Removing artifacts caused by multiple adverse weather, including rain, fog, and snow, is crucial for image processing in outdoor environments. Conventional high-performing methods face challenges, such as requiring pre-specification of weather types and slow processing times. In this study, we propose a novel convolutional neural network-based hierarchical encoder-decoder model that addresses these issues effectively. Our model utilizes knowledge of feature representations obtained from masked-based pre-training on a large-scale dataset. To remove diverse degradations efficiently, we employ a proposed dual-pooling adaptive convolution, which improves representational capability of weight generating network by using average pooling, max pooling, and filter-wise global response normalization. Experiments conducted on both synthetic and real image datasets show that our model achieves promising results. The performance on real images is also improved by a novel learning strategy, in which a model trained on the synthetic image dataset is fine-tuned to the real image dataset. The proposed method is notably cost-effective in terms of computational complexity and inference speed. Moreover, ablation studies show the effectiveness of various components in our method.

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Faster Training of Large Kernel Convolutions on Smaller Spatial Scales

Fukuzaki S., Ikehara M.

IEEE Access 12   161312 - 161328 2024年

研究論文（学術雑誌）

　概要を見る

Computational requirements for training state-of-the-art neural network models are increasing on vision tasks because high computational factors have become known to be effective in improving quality. While research in the image-processing field requires a lot of trials, this trend makes proving hypotheses difficult for researchers in computationally restricted environments. Neural convolution with a wide receptive field is one of the high-computational factors with quality improvement. This study aims to accelerate the training of the large kernel convolutions by resizing both training images and convolution filters to a smaller scale. Applying this strategy requires careful training designs to replace conventional training on the target scale, and we propose four techniques to improve the quality of the trained models. In our experiment, we apply our proposals to train an image classifier model modified from RepLKNet-31B on the image classification task of the CIFAR-10, CIFAR-100, and STL-10 datasets. Our proposed framework trains almost the same models 4.62-4.91 times faster than the standard training on the target spatial scale, keeping its accuracy, and provides 2.61-2.79 times further training acceleration and stability in accuracy compared to Progressive Learning. In addition to the training acceleration, our framework can simultaneously train models for multiple scales without any scale-specific tuning, which provides scalable usage considering the computational costs.

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Pose-aware Disentangled Multiscale Transformer for Pose Guided Person Image Generation

Shibasaki K., Ikehara M.

European Signal Processing Conference    506 - 510 2023年

ISSN  22195491

　概要を見る

Pose Guided Person Image Generation (PGPIG) is the task that transforms the pose of a person's image from the source image, its pose information, and the target pose information. Most existing PGPIG methods require additional pose information or tasks, which limits their application. Moreover, they use CNNs, which can only extract features from neighboring pixels and cannot consider the consistency of the entire image. This paper proposes a PGPIG network solving these problems by using a module containing Axial Transformers with large receptive field. The proposed method disentangles the PGPIG task into two subtasks: “rough pose transformation” and “detailed texture generation”. In the former task, low-resolution feature maps are transformed by blocks containing Axial Transformer. The latter task uses a CNN network with Adaptive Instance Normalization. Experiments show that the proposed method has competitive performance with other state-of-the-art methods. Furthermore, despite achieving excellent performance, the proposed network has a significantly fewer parameters than existing methods.

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Pose-Aware Disentangled Multiscale Transformer for Pose Guided Person Image Generation

Shibasaki K., Ikehara M.

IEEE Access 11   146054 - 146064 2023年

　概要を見る

Pose Guided Person Image Generation (PGPIG) is a task that involves generating an image of a person in a target pose, given an image in a source pose, source pose information and target pose information. Many of the existing PGPIG techniques need extra pose-related data or tasks, which cause the limitations on their applicability. In addition CNNs are used as the feature extractor which do not have long-range dependency. However, CNNs can only extract features from neighboring pixels and cannot consider image consistency. This paper introduces a PGPIG network that solves these challenges by incorporating modules that use Axial Transformers with wide receptive fields. The proposed approach disentangles the PGPIG task into two subtasks: 'rough pose transformation' and 'detailed texture generation.' In 'rough pose transformation,' lower-resolution feature maps is processed by Axial Transformer-based blocks. These blocks employ an Encoder-Decoder structure, which allows the network to use the pose information well and improves the stability and performance of the training. The latter subtask employs a CNN network with Adaptive Instance Normalization. Experimental results show the competitive performance of the proposed method compared to existing methods. The proposed method achieves lowest LPIPS in Deep Fashion dataset and FID in Market-1501 dataset. Remarkably, despite the great results obtained, the number of parameters of the proposed network is significantly less in contrast to existing methods.

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Low-Light Image Enhancement Using a Simple Network Structure

Matsui T., Ikehara M.

IEEE Access 11   65507 - 65516 2023年

　概要を見る

Under low-light conditions, captured images can be affected by unsatisfactory lighting conditions. Low-light image enhancement called LLIE is a digital image processing to obtain natural normal-light images from the low-light image. LLIE includes three main tasks: reducing noise and artifacts, preserving edges and textures, and reproducing natural brightness and color. In recent years, many types of research have focused on deep-learning-based approaches that can achieve excellent performance. However, one primary problem with these methods is that inference time is long owing to complex network structures. To solve the trade-off between the performance and implementation time, we propose a simple network with effective modules. We utilize a U-Net structure and pre-processing is added to preserve edges and textures. Moreover, we embed Channel Attention to restore color and illumination, Res FFT-ReLU to reduce noise, and Pixel Shuffler to preserve the high-frequency components. According to our experimental results, the proposed method achieves better performance and faster inference time than conventional LLIE methods.

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Inverted Residual Fourier Transformation for Lightweight Single Image Deblurring

Yae S., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2023-January 2023年

ISSN  0747668X

　概要を見る

In recent years, encoder-decoder structures are widely used for single image deblurring and successfully restore high quality images. How-ever, FLOPs and the number of parameters tend to increase to restore a high-quality image. Thus, we propose a new lightweight network (IRFTNet) based on DeepRFT. This network has two features to improve performance and lightweight. First, a new backbone called Inverted Residual Fourier Transformation block (IRFTblock) based on inverted residual block is introduced to decrease computational complexity. Second, a new module called Lower Feature Synthesis (LFS) was introduced to efficiently transfer encoder information from lower layers to upper layers. These improvements resulted in a 32.98dB in PSNR on the GoPro dataset, despite approximately half FLOPs and the number of parameters of DeepRFT.

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Inverted Residual Fourier Transformation for Lightweight Single Image Deblurring

Yae S., Ikehara M.

IEEE Access 11   29175 - 29182 2023年

　概要を見る

Single image deblurring aims to restore a sharp image by removing blurred areas in the single image. Such blurred images are not only visually unpleasant, but cause various problems in many applications like image recognition. In recent years, with the development of deep learning, neural networks are used in many single image deblurring. Especially, encoder-decoder structures are widely used for single image deblurring and successfully restore high quality images. However, FLOPs and the number of parameters tend to increase to restore a high-quality image. Thus, this paper proposes a new lightweight network (IRFTNet) based on UNet, which is a widely used basic network of encoder-decoder structures. Our proposed network has three features to improve performance and lightweight. First, a new backbone called Inverted Residual Fourier Transformation block (IRFTblock) based on inverted residual block is introduced to decrease computational complexity. Second, a new module called Lower Feature Synthesis (LFS) is introduced to efficiently transfer encoder information from lower layers to upper layers. Finally, multiple outputs structure proposed in MIMO-UNet is introduced. These improvements resulted in a 32.98dB in PSNR on the GoPro dataset, despite approximately half FLOPs and the number of parameters of DeepRFT. Further ablation studies show the effectiveness of various components in our proposed model.

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Enhanced Unpaired Image-to-Image Translation via Transformation in Saliency Domain

Shibasaki K., Ikehara M.

IEEE Access 11   137495 - 137505 2023年

　概要を見る

Unpaired image to image translation is the task of converting images in unpaired datasets. The primary goal of the task is to translate a source image into the image aligned with the target domain while keeping the fundamental content. Existing researches have introduced effective techniques to translate images with unpaired datasets, focusing on preserving the fundamental content. However, these techniques have limitations in dealing with significant shape changes and preserving backgrounds that should not be transformed. The proposed method attempts to address these problems by utilizing the saliency domain for translation and simultaneously learning the translation in the saliency domain as well as in the image domain. The saliency domain represents the shape and position of the main object. The explicit learning of transformations within the saliency domain improves network's ability to transform shapes while maintaining the background. Experimental results show that the proposed method successfully addresses the problems of unpaired image to image translation and achieves competitive metrics with existing methods.

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Enhanced Residual Fourier Transformation Network for Lightweight Image Super-resolution

Yang Y., Ikehara M.

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023    826 - 832 2023年

　概要を見る

Residual blocks have been widely used in the single image super-resolution (SR), boosting the performance of SR results. The stacking of residual blocks helps to grab more information needed for the recovery of high-resolution (HR) images, but at the cost of larger model size and computational complexity. To address this problem, we propose a lightweight enhanced residual fourier transformation network (ERFTN). To ensure the network is lightweight enough while being capable of capturing both spatial and frequency information, we introduce a novel enhanced residual fourier transformation block (ERFTB) with an enhanced spatial attention (ESA) block. In addition, we adopt the contrastive loss for training acceleration without additional parameters. Extensive experiments demonstrate that our method can achieve SR performance superior to the state-of-the-art SR methods while reducing approximately half of the number of parameters (e.g., with 190K number of parameters, achieving 32.39dB in PSNR on Urban100 dataset).

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RGB-D Salient Object Detection Using Saliency and Edge Reverse Attention

Ikeda T., Ikehara M.

IEEE Access 11   68818 - 68825 2023年

　概要を見る

RGB-D salient object detection is a task to detect visually significant objects in an image using RGB and depth images. Although many useful CNN-based methods have been proposed in the past, there are some problems such as blurring of object boundaries and inability to detect important parts of objects, leading to a decrease in detection accuracy. In this paper, we propose RGB-D salient object detection using Saliency and Edge Reverse Attention(SERA), which combines the fusion of saliency and edge features with reverse attention. The reverse attention process has the effect of making it easier to capture the boundaries of objects and undetected objects by inverting the up-sampled saliency features and weighting other saliency features, and the edge reverse attention process has the effect of making salient object regions stand out by inverting the edge features and weighting saliency features. The interaction between the salient object features and the edge features enhances each other's features and refines the information on the boundary of objects and salient object regions. In addition, to make it easier to refer to the global information of an image, we introduced the Multi-Scale Interactive Module(MSIM), which is capable of acquiring information at rich scales by converting feature maps to different resolutions and interacting with them. In addition to the salient object output, supervised learning is applied to multiple edge outputs of each resolution to improve the accuracy of both salient objects and boundary areas. Experimental results on five benchmarks show that the proposed method quantitatively performs better than the conventional method, and qualitatively improves the sharpness of object boundaries and the accuracy of detecting important parts of objects.

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UNet Based Multi-Scale Recurrent Network for Lightweight Video Deblurring

Yae S., Ikehara M.

IEEE Access 11   117520 - 117527 2023年

　概要を見る

With the recent widespread use of smartphones and digital video cameras, the opportunities to handle digital video have increased significantly. However, despite improvements in the performance of the hardware, the captured video often contains information that is not necessary for the purpose of the video. In particular, factors such as camera shake and object movement can cause blurring in video. So, we propose a method to deblur a video by software processing of the video after shooting. In conventional methods, video deblurring is often performed using a network whose main task is video super-resolution. However, in super-resolution, the size of the input and output images are different, while the input and output images are the same size in deblurring. In the case of deblurring, the input images are input to the network after a simple downsampling process, which is not optimized for the same size as the input and output images. Therefore, the proposed method constructs a multi-scale network based on UNet. The UNet-based network is a successful method for single image deblurring. Because a video is a sequence of multiple images, we use a method that has been successful in single image deblurring. Furthermore, we add improvements to the network based on the structure of MPRNet. The feature extraction modules of the bottom and the second stage of the network are replaced with a single-stage UNet. These improvements resulted in a 34.80dB in PSNR and 0.973 in SSIM on the GoPro dataset despite about 75% of the FLOPs of BasicVSR++ and 3% of the FLOPs of VRT. On the DVD dataset, the proposed model achieved 34.36dB in PSNR and 0.966 in SSIM. Further ablation studies show the effectiveness of various components in our proposed model.

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Training Large Kernel Convolutions with Resized Filters and Smaller Images

Fukuzaki S., Ikehara M.

GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics    32 - 33 2023年

　概要を見る

Convolution is an essential component in neural networks for vision tasks. Convolutions on large receptive fields are more suitable than small kernel convolutions when aggregating global features in convolutional neural networks. However, large kernel convolutions require much computation and memory usage, slowing training neural networks. Then, we propose to train convolution weights with small images, resizing the convolution filters. While this idea shortens the time for training filters, simply applying this causes profound degradation. In this paper, we introduce four techniques that suppress degradation; weight scaling, removing Batch Normalization, defining a minimum resolution, and training with various-size images. In our experiment, we apply our proposals to train an image classification model based on RepLKNet-B on the image classification task of the CIFAR-100 dataset. Training with our proposals is approximately eight times faster than conventional training on the target spatial scale, keeping its accuracy.

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Toward High-Quality Real-Time Video Denoising With Pseudo Temporal Fusion Network

Shibasaki K., Ikehara M.

IEEE Access 11   81466 - 81476 2023年

　概要を見る

With the increasing availability of high-resolution video recording and streaming, there is a need for fast and high-quality video denoising methods that can handle high-resolution videos. However, many existing methods fail to achieve high-quality denoising performance and computationally effecient at the same time. This paper proposes a video denoising network, Pseudo Temporal Fusion Network (PTFN), that satisfies these requirements. PTFN adopts a new Pseudo Temporal Fusion (PTF) module that captures pseudo-temporal relationships between video frames in combination with the Temporal Shift Module. PTFN also adopts a modern ConvBlock paradigm that breaks away from the classical ConvBlock paradigm, contributing to denoising performance and computationally effecient. PTFN achieves better performance than existing video denoising methods in terms of both video quality and computational effeciency. Specifically, PTFN has only about 16.7% of the computational cost of existing lightweight methods, while it improves denoising performance. PTFN is also superior in terms of memory consumption. It can process 1080p videos with a GPU with 24 GB RAM. In addition, a lighter version (PTFN Half) can process 2K videos at high speed under the same conditions.

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Stereo Matching with Supplementary Boundary Information

Hashimoto K., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2023-January 2023年

ISSN  0747668X

　概要を見る

Stereo matching, which estimates the disparity of corresponding points in paired images, has problems with accuracy degraded by occlusion, repeated texture, texture-free areas, and gloss. To address this problem, we focused on object boundary information, which is less susceptible to such problems. This is because occlusion is more likely to occur at the boundaries, and even if the corresponding points disappear due to occlusion, the boundaries still have the corresponding parts. The boundary information is also robust to such problems because it is not affected by repeated textures, untextured areas, or gloss. Therefore, we propose a method for generating the boundary ground truth from the depth ground truth and develop a network and loss function for estimating the boundary information. We also propose that the accuracy can be improved by incorporating the estimated boundary information into a state-of-the-art network as an auxiliary.

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Single Image Fence Removal Using Fast Fourier Transform

Kume K., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2023-January 2023年

ISSN  0747668X

　概要を見る

Fences extensively cover the image in a variety of shapes, making fence removal from the image is a challenging task. Most fence removal in a single image is performed in two stages: fence area estimation and inpainting as shown in the Fig 1. Conventional fence detection has utilized information such as edge information, luminance information, and fence regularity to deal with various shapes. However, those methods have resulted in missing fences and false detections of structures similar to fences. In this paper, we propose a network that incorporates a module that includes a fast Fourier transform. This allows us to extract global features of fences that extensively cover the image in various shapes. Furthermore, we create a dataset of rectangular fences, which is not available in previous datasets. Experimental results on existing and proposed datasets show that our method achieves better results, both quantitatively and qualitatively, by suppressing missing fences and false positives of other structures.

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People Removal Using Edge and Depth Information

Yae S., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2022-January 2022年

ISSN  0747668X

　概要を見る

In this paper, we propose a people removal method from a single image for privacy and other reasons using a three-stage network of depth estimation, semantic segmentation, and inpainting, as shown in Fig. 1. In this three-stage network, we improve semantic segmentation for detecting people. We focus on a special situation of a person and construct a network. It is known that the accuracy of conventional methods can be improved by using edge information. The accuracy of segmentation can be further improved by increasing the accuracy of the edge map. In addition, edge detection does not work well when the person and the background are of the similar color, because edge detects the brightness change of the image. Therefore, in this paper, an adversarial loss function for edge maps is proposed. In addition, since an image with people is expected to have a depth difference from the background image, we use a trained depth estimation network to include the depth image in the input. In this way, it is possible to construct a network for people removal with a high accuracy both quantitatively and qualitatively.

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MULTI-STAGE FEATURE ALIGNMENT NETWORK FOR VIDEO SUPER-RESOLUTION

Suzuki K., Ikehara M.

Proceedings - International Conference on Image Processing, ICIP    2001 - 2005 2022年

ISSN  15224880

　概要を見る

Video super-resolution aims at generating high-resolution video frames using multiple adjacent low-resolution frames. An important aspect of video super-resolution is the alignment of neighboring frames to the reference frame. Previous methods directly align the frames either using optical flow or deformable convolution. However, directly estimating the motion from low-resolution inputs is hard since they often contain blur and noise that hinder the image quality. To address this problem, we propose to conduct feature alignment across multiple stages to more accurately align the frames. Furthermore, to fuse the aligned features, we introduce a novel Attentional Feature Fusion Block that applies a spatial attention mechanism to avoid areas with occlusion or misalignment. Experimental results show that the proposed method achieves competitive performance to other state-of-the-art super-resolution methods while reducing the network parameters.

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Local Image Denoising Using RAISR

Zin T., Seta S., Nakahara Y., Yamaguchi T., Ikehara M.

IEEE Access 10   22420 - 22428 2022年

　概要を見る

Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems, we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.

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Enhanced Multiscale Attention Network for Single Image Dehazing

Imai W., Ikehara M.

IEEE Access 10   93626 - 93635 2022年

　概要を見る

Under severe weather conditions, the quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the images, haze removal methods play a critical role. The most difficult part of haze removal is removing the haze that spreads over the entire image. Many CNN-based methods have been proposed to remove the haze, and can be divided into two types. One is to use a multi-scale structure and the other is to stack layers. The former causes image degradation due to the loss of some of the original information in an image and the latter increases computational complexity due to not reducing the resolution. In addition, a large number of parameters is required to secure the expressive power of the model, which leads to a huge amount of memory. To tackle these problems, we tried to 1) downsample the image while saving parameters and maintaining the quality of the generated image, and 2) consider the information in the entire image to remove the haze. For the first problem, we tried to solve this by using a feature extractor that has been used in other tasks, learning to optimize the output image in low-resolution, and preparing kernels with various dilation rates to expand the receptive fields. For the second problem, we use the attention structure to determine which part of the image features should be focused on from the entire feature map. By incorporating such modules, our method achieves better results on both synthetic and real-world images when compared with state-of-the-art methods.

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Channel Attention GAN Trained with Enhanced Dataset for Single-Image Shadow Removal

Abiko R., Ikehara M.

IEEE Access 10   12322 - 12333 2022年

　概要を見る

Even today, where many deep-learning-based methods have been published, single-image shadow removal is a challenging task to achieve high accuracy. This is because the shadow changes depending on various conditions such as the target material or the light source, and it is difficult to estimate all the physical parameters. In this paper, we propose a new single-image shadow removal method (Channel Attention GAN: CANet) using two networks for detecting shadows and removing shadows. Intensity change in shadowed regions has different characteristics depending on the wavelength of light. In addition, the image acquisition system of the camera acquires an image in a state where the RGB values influence each other. Therefore, our method focused on the physical properties of shadows and the camera's image acquisition system. The proposed network has a structure considering the relationship between color channels. When training this network, we modified the color and added some artifacts to the training images in order to make the training dataset more complex. These image processing are based on the shadow model, considering the camera image acquisition system. With these new proposals, our method can remove shadows in all ISTD, ISTD+, SRD, and SRD+ datasets with higher accuracy than the state-of-the-art methods. The code is available on GitHub: https://github.com/ryo-abiko/CANet.

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4K Real Time Image to Image Translation Network With Transformers

Shibasaki K., Fukuzaki S., Ikehara M.

IEEE Access 10   73057 - 73067 2022年

　概要を見る

CNNs have traditionally been applied in computer vision. Recently, applying Transformer networks, originally a technique in natural language processing, to computer vision has received much attention and produced superior results. However, Transformers and their derivation have drawbacks that the computational cost and memory usage increase rapidly with the image resolution. In this paper, we propose the Laplacian Pyramid Translation Transformer (LPTT) for image to image translation. The Laplacian Pyramid Translation Network, a previous study of this work, creates Laplacian pyramid of the input images and processes each component with CNNs. However, LPTT transforms the high-frequency components with CNNs and the low-frequency components with Axial Transformer blocks. LPTT can have Transformer's expressive power while reducing the computational cost and memory usage. LPTT significantly improves the quality of generated images and inference speed for high-resolution images over conventional methods. LPTT is the first network with a Transformer that can perform practical inference in real time on 4K resolution images. LPTT can also process 8K images in real time depending on the model conditions and the performance of the GPU. The ablation study in this paper suggests that even when processing high-resolution images, the performance is improved while maintaining the inference speed by computing the low-resolution component with a Transformer. LPTT improves PSNR value by 0.41 dB in MIT-Adobe FiveK dataset. The greater the number of layers in the Laplacian pyramid, the greater the improvement of LPTT over the Laplacian Pyramid Translation Network.

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Principal Components of Neural Convolution Filters

Fukuzaki S., Ikehara M.

IEEE Access 10   104328 - 104336 2022年

　概要を見る

Convolutions in neural networks are still essential on various vision tasks. To develop neural convolutions, this study focuses on Structured Receptive Field (SRF), representing a convolution filter as a linear combination of widely acting designed components. Although SRF can represent convolution filters with fewer components than the number of filter bins, N-Jet, the sole component system implementation, requires ten trainable parameters per filter to improve accuracy even for 3×3 convolutions. Hence, we aim to formulate a new component system for SRF that can represent valid filters with fewer components. Our component system named 'OtX' is based on the Principal Component Analysis of well-trained filter weights because the extracted components will also be principal for neural convolution filters. In addition to proposing the component system, we develop a component scaling method to defuse massive scale differences among the coefficients in a linear combination of OtX components. In the experimental section, we train image classification models on CIFAR-100 dataset under the hyperparameters tuned for the original models with the standard convolutions. For NFNet-F0 classifier, OtX with six components performs 0.5% better than the standard convolution, 3.1% better than N-Jet with six components, and only 0.1% worse than N-Jet with ten components. Besides, OtX with nine components provides stabler training than N-Jet, performing 0.5% better than the standard for NFNet-F0. OtX suits when replacing standard convolutions because OtX performs at least comparably against N-Jet with further parameter efficiency and training stability.

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Single Image Raindrop Removal Using a Non-Local Operator and Feature Maps in the Frequency Domain

Ezumi S., Ikehara M.

Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022    1343 - 1350 2022年

　概要を見る

Taking a photo on a rainy day may result in a photo with raindrops. Images containing raindrops have a significant impact on the visual impression and accuracy when applied to image recognition systems. Thus, an automatic high-quality raindrop removal method is desired for outdoor image processing systems as well as for acquiring good-looking images. Several existing methods have been proposed to tackle this problem, but they often fail to keep global consistency and generate unnatural patterns. In this paper, we tackle this prob-lem by introducing a non-local operator. The non-local operator combines features in distant locations with matrix multiplication and enables consistency in distant locations. In addition, high-frequency components such as edges are more affected in images with raindrops. Inspired by the nature that high-frequency components can be separated from other components in the frequency domain, we also propose to process feature maps in the frequency domain, which are obtained by the fast Fourier transform operation and processed by several convolution layers. Experimental results show that our method effectively removes raindrops and achieves state-of-the-art performance.

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Single Image Raindrop Removal Using a Non-Local Operator and Feature Maps in the Frequency Domain

Ezumi S., Ikehara M.

IEEE Access 10   91976 - 91983 2022年

　概要を見る

Taking a photo on a rainy day may result in a photo with raindrops. Images containing raindrops have a significant impact on the visual impression and accuracy when applied to image recognition systems. Thus, an automatic high-quality raindrop removal method is desired for outdoor image processing systems as well as for acquiring good-looking images. Several existing methods have been proposed to tackle this problem, but they often fail to keep global consistency and generate unnatural patterns. In this paper, we tackle this problem by introducing a non-local operator. The non-local operator combines features in distant locations with matrix multiplication and enables consistency in distant locations. In addition, high-frequency components such as edges are more affected in images with raindrops. Inspired by the nature that high-frequency components can be separated from other components in the frequency domain, we also propose to process feature maps in the frequency domain, which are obtained by the fast Fourier transform operation and processed by several convolution layers. Experimental results show that our method effectively removes raindrops and achieves state-of-the-art performance.

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Scale Recurrent Network for Single Image Dehazing

Imai W., Ueki Y., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2022-January 2022年

ISSN  0747668X

　概要を見る

The quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the image, haze removal methods play a critical role. In this paper, we propose a new multi-scale network structure that is rarely used in haze removal and a new loss function to generate high-quality haze-free images. Despite a number of proposed dehazing methods, they are not able to capture the haze accurately without being confused by other objects that are similar in color to haze and completely remove the haze throughout the image. Our proposed network architecture takes the Scale Recurrent Network structure and we incorporate dilated convolution to catch the more global features like haze with the wide receptive field. Additionally, we train our network with a new loss function using dark channel prior to more effectively learn the haze features. Compared with state-of-the-art methods, our proposed method achieves better results on both synthetic and real-world images.

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Saturated Reflection Detection for Reflection Removal Based on Convolutional Neural Network

Yoshida T., Funahashi I., Yamashita N., Ikehara M.

IEEE Access 10   39800 - 39809 2022年

　概要を見る

Single image reflection removal is a technique that removes undesirable reflections, which occur due to glass, from images. Various methods of reflection removal have been proposed, but unfortunately, they usually fail to remove reflections particularly with very high pixel values. In this paper, we define these saturated reflections and their characteristics, as well as discuss and propose a removal system. The proposed system detects areas of saturated reflections based on our proposed model of convolutional neural networks and restores them by a conventional method of image estimation. In our experiments, the proposed system shows better peak-signal-to-noise ratio scores and perceptual quality than conventional methods of reflection removal.

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Improved image denoising via RAISR with fewer filters

Zin T., Nakahara Y., Yamaguchi T., Ikehara M.

Computational Visual Media 7 （ 4 ） 499 - 511 2021年12月

ISSN  20960433

　概要を見る

In recent years, accurate Gaussian noise removal has attracted considerable attention for mobile applications, as in smart phones. Accurate conventional denoising methods have the potential ability to improve denoising performance with no additional time. Therefore, we propose a rapid post-processing method for Gaussian noise removal in this paper. Block matching and 3D filtering and weighted nuclear norm minimization are utilized to suppress noise. Although these nonlocal image denoising methods have quantitatively high performance, some fine image details are lacking due to the loss of high frequency information. To tackle this problem, an improvement to the pioneering RAISR approach (rapid and accurate image super-resolution), is applied to rapidly post-process the denoised image. It gives performance comparable to state-of-the-art super-resolution techniques at low computational cost, preserving important image structures well. Our modification is to reduce the hash classes for the patches extracted from the denoised image and the pixels from the ground truth to 18 filters by two improvements: geometric conversion and reduction of the strength classes. In addition, following RAISR, the census transform is exploited by blending the image processed by noise removal methods with the filtered one to achieve artifact-free results. Experimental results demonstrate that higher quality and more pleasant visual results can be achieved than by other methods, efficiently and with low memory requirements. [Figure not available: see fulltext.].

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Gan-based rain noise removal from single-image considering rain composite models

Matsui T., Ikehara M.

European Signal Processing Conference 2021-January   665 - 669 2021年01月

ISSN  22195491

　概要を見る

Under severe weather conditions, outdoor images or videos captured by cameras can be affected by heavy rain and fog. In this paper, we address a single-image rain removal problem (de-raining). As compared to video-based methods, single-image based methods are challenging because of the lack of temporal information. Although many existing methods have tackled these challenges, they suffer from overfitting, over-smoothing, and unnatural hue change. To solve these problems, we propose a GAN-based de-raining method. The optimal generator is determined by experimental comparisons. To train the generator, we learn the mapping between rainy and residual images from the training dataset. Besides, we synthesize a variety of rainy images to train our network. In particular, we focus on not only the orientations and scales of rain streaks but also the rainy image composite models. Our method also achieves better performance on both synthetic and real-world images than state-of-the-art methods in terms of quantitative and visual performances.

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Shadow detection and removal using GAN

Nagae T., Abiko R., Yamaguchi T., Ikehara M.

European Signal Processing Conference 2021-January   630 - 634 2021年01月

ISSN  22195491

　概要を見る

To remove shadowed region in a single image, it is important to obtain high accuracy in both two processes, shadow detection and removal. In order to improve the results, recent methods perform these two processes simultaneously and use GAN for the training. However, since these methods do not try to maintain the luminance of non-shadowed regions, the output images tend to be faded. In this paper, to overcome fading problem, we proposed a new GAN structure based on shadow model. Since our GAN-based method focus on the variation of the illuminance, the illuminances of the shadowed regions, whose amount of change are large, are effectively estimated. In addition, non-shadowed regions remain slightly faded due to our new GAN structure and training method. Owing to our novel GAN structure and training method, our method outperforms state-of-the-art methods in PSNR and SSIM.

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Weighted generalization of dark channel prior with adaptive color correction for defogging

Ueki Y., Ikehara M.

European Signal Processing Conference 2021-January   685 - 689 2021年01月

ISSN  22195491

　概要を見る

Images and video captured in water or fog suffer from low contrast and color distortion due to light scattering and absorption. An image formation model for hazy images is commonly used to restore both underwater images and hazy images because of the similarity between the two types of images. However, red light is attenuated faster than blue and green light in underwater, and underwater images are distorted by changes of color tone. Therefore, most current methods are specialized for either hazy images or underwater images. In this paper, we propose a novel defogging method which is efficient for both hazy images and underwater images. Our method is composed of adaptive color correction and weighted generalization of dark channel prior (WGDCP). Experimental results show that our algorithm can recover both underwater images and hazy images.

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GAN-Based Image Deblurring Using DCT Loss with Customized Datasets

Tomosada H., Kudo T., Fujisawa T., Ikehara M.

IEEE Access 9   135224 - 135233 2021年

　概要を見る

In this paper, we propose a high quality image deblurring method that uses discrete cosine transform (DCT) and requires less computational complexity. We train our model on a new dataset which is customized to include images with large motion blurs. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale and multi-patch architectures of CNN restore blurred images clearly and suppress more ringing or blocking artifacts, but they take a longer time to process. To improve the quality of deblured images and reduce the computational time, we propose a method called 'DeblurDCTGAN' that preserves texture and suppresses ringing artifacts in the restored image without multi-scale or multi-patch architecture using DCT based loss. This loss compares the restored image and the ground truth image in the frequency domain. With this loss, DeblurDCTGAN can reduce block noise and ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances in terms of PSNR, SSIM, and running time compared with conventional methods. In terms of real image datasets, DeblurDCTGAN shows a better performance by using a customized training dataset made from GoPro, DVD, NFS and HIDE training datasets. Experimented code with pre-trained weights, datasets and results are available at https://github.com/Hiroki-Tomosada/DCTGAN-master

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High Reflection Removal Using CNN with Detection and Estimation

Funahashi I., Yamashita N., Yoshida T., Ikehara M.

2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings    1381 - 1385 2021年

　概要を見る

In this paper, we propose a method of reflection removal that reduces high intensity reflection for single image. Various methods of reflection removal have been proposed, but they fail to reduce the high reflections due to their assumption. To tackle this issue, the proposed method detects the target areas with high reflections by the proposed convolutional neural network (CNN) model and estimates their background information by inpainting. It is observed that the reflection is strongly blurred because of its physical property, and hence the proposed CNN model utilizes edge features of pixels for the detection. In simulation, we compare state-of-the-art methods of reflection removal with and without the proposed method for natural images, and the proposed method improves peak signal-to-noise ratio and perceptual quality.

Underwater Image Enhancement with Multi-Scale Residual Attention Network

Ueki Y., Ikehara M.

2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings 2021年

　概要を見る

Underwater images suffer from low contrast, color distortion and visibility degradation due to the light scattering and attenuation. Over the past few years, the importance of underwater image enhancement has increased because of ocean engineering and underwater robotics. Existing underwater image enhancement methods are based on various assumptions. However, it is almost impossible to define appropriate assumptions for underwater images due to the diversity of underwater images. Therefore, they are only effective for specific types of underwater images. Recently, underwater image enhancement algorisms using CNNs and GANS have been proposed, but they are not as advanced as other image processing methods due to the lack of suitable training data sets and the complexity of the issues. To solve the problems, we propose a novel underwater image enhancement method which combines the residual feature attention block and novel combination of multi-scale and multi-patch structure. Multi-patch network extracts local features to adjust to various underwater images which are often Non-homogeneous. In addition, our network includes multi-scale network which is often effective for image restoration. Experimental results show that our proposed method outperforms the conventional method for various types of images.

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Multiplierless lifting-based fast X transforms derived from fast Hartley transform factorization

池原 雅章

MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 29 （ 1 ） 99 - 118 2018年01月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  0923-6082

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Sub-Pixel Shift Estimation of Image Based on the Least Squares Approximation in Phase Region

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E101A （ 1 ） 267 - 272 2018年01月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  1745-1337

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Detail Preserving Mixed Noise Removal by DWM Filter and BM3D

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A （ 11 ） 2451 - 2457 2017年11月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  1745-1337

• Access to Document (DOI)

Random-Valued Impulse Noise Removal Using Non-Local Search for Similar Structures and Sparse Representation

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A （ 10 ） 2146 - 2153 2017年10月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  1745-1337

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Image Restoration of JPEG Encoded Images via Block Matching and Wiener Filtering

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A （ 9 ） 1993 - 2000 2017年09月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  1745-1337

• Access to Document (DOI)

Dual-DCT-Lifting-Based Lapped Transform with Improved Reversible Symmetric Extension

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A （ 5 ） 1109 - 1118 2017年05月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  1745-1337

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Blind Image Deconvolution Using Specified 2-D HPF for Feature Extraction and Conjugate Gradient Method in Frequency Domain

Takanori Fujisawa and IKEHARA MASAAKI

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E100-A （ 3 ） 846 - 853 2017年03月

研究論文（学術雑誌）, 共著, 査読有り

Dynamic Bandwidth Allocation Supporting Diversified Queue Status Reporting for EPON Systems

Murayama Daisuke, Suzuki Kenichi, Kubo Ryogo and IKEHARA MASAAKI

JOURNAL OF OPTICAL COMMUNICATIONS AND NETWORKING 8 （ 8 ） 530 - 539 2016年08月

研究論文（学術雑誌）, 共著, 査読有り

Single Image Super Resolution by L2 Approximation with Random Sampled Dictionary

Takanori Fujisawa, Taichi Yoshida, Kazu Mishiba, and IKEHARA MASAAKI

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences E99-A （ 2 ） 612 - 620 2016年02月

研究論文（学術雑誌）, 共著, 査読有り

Reversible symmetric nonexpansive convolution: an effective image boundary processing for mbiM -channel lifting-based linear-phase filter banks

池原 雅章

IEEE transactions on Image Processing Vol23 （ No.6 ） 2744 - 2749 2014年06月

研究論文（学術雑誌）, 共著, 査読有り

Dynamic Bandwidth Allocation with PID-Based Request Threshold Controller for EPON Systems

池原 雅章

ITE Transactions on Media Technology and Applications Vol.2 （ No.4 ） 320 - 326 2014年04月

研究論文（学術雑誌）, 査読有り

Adaptive Reversible Data Hiding via Integer-to-Integer Subband Transform and Adaptive Generalized Difference Expansion Method

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E97A （ 1 ） 384 - 392 2014年01月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  0916-8508

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Integer fast lapped transforms based on direct-lifting of DCTs for lossy-to-lossless image coding

池原 雅章

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING    3220 - 3228 2013年12月

研究論文（学術雑誌）,  ISSN  1687-5281

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Content Aware Image Resizing with Constraint of Object Aspect Ratio Preservation

池原 雅章

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E96D （ 11 ） 2427 - 2436 2013年11月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  0916-8532

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Two Dimensional M-Channel Non-separable Filter Banks Based on Cosine Modulated Filter Banks with Diagonal Shifts

池原 雅章

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E96A （ 8 ） 1685 - 1694 2013年08月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  0916-8508

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Improved Seam Merging for Content-Aware Image Resizing

池原 雅章

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E96D （ 2 ） 349 - 356 2013年02月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  0916-8532

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Generalized Block-Lifting Factorization of M-Channel Biorthogonal Filter Banks for Lossy-to-Lossless Image Coding

池原 雅章

IEEE TRANSACTIONS ON IMAGE PROCESSING 21 （ 7 ） 3220 - 3228 2012年07月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  1057-7149

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Two dimensional non-separable adaptive directional lifting structure of discrete wavelet transform

池原 雅章

IEICE Trans. Fundamentals vol. E94-A （ No.10 ） 1920-1927 2011年10月

研究論文（学術雑誌）, 単著, 査読有り

A linear optimization of dual-tree complex wavelet transform

池原 雅章

IEICE Trans. Fundamentals vol. E94-A （ No.6 ） 1386-139 2011年06月

研究論文（学術雑誌）, 単著, 査読有り

ウェーブレット変換領域におけるシームカービング

三柴 数, 池原 雅章

映像情報メディア学会誌 Vol.65 （ No.5 ） 698-703 2011年05月

研究論文（学術雑誌）, 単著, 査読有り

フラッシュ画像とノンフラッシュ画像の色彩と陰影の変換による鮮鋭画像生成法

白井啓一郎, 池原雅章, 岡本正行

電子情報通信学会論文誌(A) vol. J94-A （ No.4 ） 275-284 2011年04月

研究論文（学術雑誌）, 単著, 査読有り

A simplified lattice structure of two dimensional generalized lapped orthogonal transform

池原 雅章

IEICE Trans. Fundamentals E94-A （ 2 ） 671-679 2011年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

画素の接続関係を考慮した非線形画像リサイズ

三柴数,池原 雅章

電子情報通信学会論文誌(D) Vol. J94-D （ No.11 ） 356-364 2011年01月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Integer DCT based on direct-lifting of DCT-IDCT for lossless-to-lossy image coding

池原 雅章

IEEE Trans. Image Process Vol.19 （ No.11 ） 2958-2965 2010年11月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

ブロックベースシームカービングを用いた画像のリサイズ

三柴数,池原 雅章

電子情報通信学会論文誌(A) Vol. J93-A （ No.9 ） 605-612 2010年09月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

M-channel paraunitary filter banks based on direct lifting structure of building block and its inverse transform for lossless-to-lossy image coding

池原 雅章

IEICE Trans. Fundamentals Vol. E93-A （ No.8 ） 1457-1464 2010年08月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Realization of IntDCT with arbitrary block size using relation between DCT-IV and parallel block system of DCT-II

池原 雅章

Journal of Signal Processing Vol.14 （ No.4 ） 289-292 2010年07月

研究論文（研究会，シンポジウム資料等）, 単著

Image interpolation using edge-directed smoothness measure filter

池原 雅章

IEICE Trans. Info. Syst. E93-D （ No.6 ） 1618-1624 2010年06月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Integer discrete cosine transform via lossless Walsh-Hadamard transform with structural regularity for low-bit-word-length

池原 雅章

IEICE Trans. Fundamentals vol. E93-A （ no. 4 ） pp. 734-741 2010年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Adaptive directional wavelet transform based on directional prefiltering

池原 雅章

IEEE Trans. Image Process. vol. 19 （ no. 4 ） pp. 934-945 2010年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A class of near shift-invariant and orientation-selective transform based on delay-less oversampled even-stacked cosine-modulated filter banks

池原 雅章

IEICE Trans. Fundamentals vol. E93-A （ no. 4 ） pp. 724-733 2010年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

カラー画像平滑化のための色線形性を考慮した異方性拡散

白井啓一郎, 庄司久人, 池原雅章

電子情報通信学会論文誌(A) vol. J93-A （ no. 3 ） pp. 181-189 2010年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

ブロックリフティング分解を用いたM分割整数離散コサイン変換

鈴木大三, 池原雅章

電子情報通信学会論文誌(A) vol. J92-A （ no. 11 ） pp. 888-900 2009年11月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

並列リフティング構造を用いたM分割完全再構成フィルタバンク

鈴木大三, 池原雅章

電子情報通信学会論文誌(A) vol. J92-A （ no. 10 ） pp. 659-668 2009年10月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Higher-order feasible building blocks for lattice structure of oversampled linear-phase perfect reconstruction filter banks

池原 雅章

Signal Processing （Elsevier）  vol. 89 （ no. 9 ） pp. 1694-1703 2009年09月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A novel design of regular cosine-modulated filter banks for image coding

池原 雅章

IEICE Trans. Fundamentals vol. E92-A （ no. 7 ） pp. 1633-1641, 2009 2009年07月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A bottom-up design approach of critically sampled contourlet transform for efficient image representation

池原 雅章

IEICE Trans. Fundamentals （IEICE）  vol. E92-A （ no. 3 ） pp. 762-771, 2009 2009年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multiresolution image representation using combined 2-D and 1-D directional filter banks

池原 雅章

IEEE Trans. Image Process. （IEEE）  vol. 18 （ no. 2 ） pp. 269-280, 2009 2009年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

M分割双直交フィルタバンクのブロックリフティング分解

岩村俊輔, 田中雄一, 池原雅章

電子情報通信学会論文誌A vol. J91-A （ no. 12 ） pp. 1181-1189 2008年12月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A lattice structure of biorthogonal linear-phase filter banks with higher order feasible building blocks

池原 雅章

IEEE Trans. Circuits Syst. I: Regular Papers, vol. 55 （ no. 8 ） pp. 2322-2331 2008年08月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

二分割複素係数擬直交フィルタバンク

京地清介, 田中雄一, 池原雅章

電子情報通信学会論文誌A vol. J91-A, （ no. 4 ） pp. 458-467, 2008. 2008年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

3D avatar modeling and transmission using cylinder mapping

池原 雅章

WSEAS Trans. Elec. vol. 55 （ no. 3 ） pp. 57-64 2008年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

構造的にレギュラリティ条件を満たした一次線形位相フィルタバンクの設計と画像符号化への応用

田中雄一, 池原雅章

電子情報通信学会論文誌A vol. J91-A （ no. 2 ） pp. 192-201 2008年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

HMM-based surface reconstruction from single images

池原 雅章

Systems and Computers in Japan 38 （ 11 ） 80-89 2007年11月

研究論文（研究会，シンポジウム資料等）, 単著

3Dモデル形状とテクスチャ品質保持のためのメッシュパラメータ化

白井啓一郎, 伊藤尚徳, 池原雅章, 奥田正浩

電子情報通信学会論文誌A vol. J90-A （ no. 9 ） pp. 705-714, 2007 2007年09月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Design of M-channel perfect reconstruction filter banks with IIR-FIR hybrid building blocks

池原 雅章

IEICE Trans. Fundamentals vol. E90-A （ no. 8 ） pp. 1636-1643 2007年08月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

M-channel nonlinear phase filter banks in image compression: structure, design, and signal extension

池原 雅章

IEEE Trans. Signal Process., vol. 55 （ no. 4 ） pp. 1339-1351 2007年04月

研究論文（研究会，シンポジウム資料等）, 単著

リフティング構造に基づくパラユニタリフィルタバンクの設計とロッシー及びロスレス画像符号化への応用

池原 雅章

電子情報通信学会論文誌A vol. J89-A （ no. 11 ） pp. 950-959 2006年11月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Cylindrical approximation of 3D meshes and its application to similarity detection

池原 雅章

Journal of Signal Processing vol.10 （ no.5 ） pp.363-370 2006年10月

研究論文（研究会，シンポジウム資料等）, 単著

3Dモデルの高速な円筒ジオメトリ画像化法," 信学論(A),

白井啓一郎, 奥田正浩, 池原雅章

電子情報通信学会論文誌A vol. J89-A （ no. 7 ） pp. 629-638 2006年07月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Design of equiripple minimum phase FIR filters with ripple ratio control

池原 雅章

IEICE Trans. Fundamentals vol. E89-A （ no. 3 ） pp. 751-756 2006年03月

研究論文（研究会，シンポジウム資料等）, 単著

ウェーブレット変換による合焦画像の作成

白井啓一郎, 野村和史,池原 雅章

電子情報通信学会論文誌A J88-A （ 10 ） 1154-1162 2005年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Complex approximation of FIR digital filters by updating desired responses

池原 雅章

IEEE Trans.os Signal Processing 53 （ 8 ） 2948 – 2953 2005年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

冗長性を削減した不等長線形位相フィルタバンクの設計

田中雄一, 越智章裕, 池原 雅章

電子情報通信学会論文誌A J88-A （ 9 ） 1035-1044 2005年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

非線形位相パラユニタリフィルタバンクのための非拡張畳込みと画像符号化への応用

田中雄一, 越智章裕,池原 雅章

電子情報通信学会論文誌A J88-A （ 7 ） 814-821 2005年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

制約の少ない一般化線形位相完全再構成不等長フィルタバンクの設計

越智章裕, 池原 雅章

電子情報通信学会論文誌A J88-A （ 6 ） 704-713 2005年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

位相相関動き補償を用いた動画像三次元符号化法

小栗知義, 池原 雅章、トロン 　グエン

電子情報通信学会論文誌A J88-A （ 4 ） 528-534 2005年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

FIRディジタルフィルタの振幅・群遅延同時近似

赤澤　穣, 池原 雅章

電子情報通信学会論文誌 A J87-A （ 11 ） 1395-1402 2004年11月

研究論文（研究会，シンポジウム資料等）, 共著

帯域内相関に基づく符号化を考慮した不等長フィルタバンクの設計

宇戸寿幸、萩原隆、池原 雅章

電子情報通信学会論文誌 A J87-A （ 7 ） 1082-1085 2004年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Design of IIR digital filters in the complex domain by transforming the desired response

池原 雅章

IEEE trans. on Signal Processing 52 （ 7 ） 1975-1982 2004年07月

研究論文（学術雑誌）, 単著

Design of Generalized Filter Banks with Unequal-Length and its Application of Image Coding

池原 雅章

IEICE Trans. Fundamental E87-A （ 7 ） 1756-1764 2004年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Quasi-equiripple Approximation of Minimum phase FIR filters by updating desired response

池原 雅章

IEE Proceedings of Vision and Image signal processing 151 （ 3 ） 164-169 2004年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

M分割直交フィルタバンクを用いた画像符号化-レギュラリティを考慮した設計と信号拡張-

岡知博、宇戸寿幸、池原 雅章

電子情報通信学会論文誌A J87-A （ 5 ） 597-605 2004年05月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Ｍチャンネルパラユニタリフィルタバンクの新しいラティス構造

小林　悠浩、池原 雅章

電子情報通信学会論文誌A J87-A （ 4 ） 492-499 2004年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

ミラー周波数にゼロ点を持つ一般化線形位相パラユニタリフィルタバンクの設計

越智　章裕、小林悠浩池原 雅章

電子情報通信学会論文A J87-A （ 4 ） 483-491 2004年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Decorrelating Compensation Scheme for Coe ・cient Errors of a Filter Bank Parallel A/D Converter

池原 雅章

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 3 （ 2 ） 341-347 2004年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

ウエーブレットパケットによる画像符号化方式

宇戸　寿幸池原 雅章

画像ラボ 15 （ 1 ） 1-5 2004年01月

研究論文（学術雑誌）, 単著

Measuring Errors on 3D Meshes using Pixel Based Search

K.Inagaki, M.Okuda, M.Ikehara, S.Takahashi

IEICE Trans. Inf. & Syst. Vol.E86-D （ No.9 ） 1903-1908 2003年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

画素分割を用いた３Dメッシュの新しい誤差計測法を与えた。

HMMを用いた単一画像からの物体形状復元

長井隆行、池原 雅章、榑松明

電子情報通信学会論文誌 D J86-D-II （ No.9 ） 1286-1296 2003年09月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

　概要を見る

多数の画像とその３D情報のデータベースからHMMでモデルを作成し、単一画像のみからモデルにより３D画像を復元する新しい手法を提案した。

テクスチャ付き３Ｄメッシュの正則化

奥田　正浩、高橋　進一池原 雅章

画像ラボ 14 （ 6 ） 1-4 2003年06月

研究論文（学術雑誌）, 単著

Compression of 3D models by remesh on Texture images

IEICE Trans. D, Vol.E86-D （ No.6 ） 1110-1115 2003年06月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

一般化線形位相パラユニタリ不等分割フィルタバンク

小林、川田、池原 雅章

電子情報通信学会論文誌A, Vol.J86-A （ No.6 ） pp.638-645 2003年06月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

直交および双直交マルチウェーブレットの設計と実現

宇戸、中原、池原 雅章

電子情報通信学会論文誌A, Vol.J86-A （ No.4, ） 402-409 2003年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

直交フィルタバンクに対する信号拡張法とその画像符号化への応用

宇戸寿之、池原 雅章

電子情報通信学会論文誌A, Vol.J86-A （ No.3 ） pp.199-206 2003年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

クワッドツリー分割に基づく画像符号化法を用いたウェーブレットパケット

宇戸、奥田、池原 雅章

電子情報通信学会論文誌A, Vol.J86-A （ No.2 ） 111-118 2003年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A smooth extension for the nonexpansive orthogonal wavelet , decomposition of finite length signals

T. Uto, M. Ikehara

IEEE ICASSP2003, Vo3   625 -628 2003年

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Group delay approximation of allpass digital filters by transforming the desired response

T.Matsunaga, M.Ikehara

IEEE ICASSP2003, Vo6   393 -396 2003年

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Digital Compensation Scheme for Coefficient Errors of Complex Filter Bank Parallel A/D Converter in Low-IF Receivers

Y.Sanada, M.Ikehara

IEICE Trans. Communications, Vol.E85-B （ No.12 ） 2656-2662 2002年12月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A family of lapped regular transforms with integer coefficients

M.Ikehara, T.D.Tran, T.Q.Nguyen

IEEE Transactions on Signal Processing, Vol.50, （ No.4 ） 834 -841 2002年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

リフティングを用いた一般化重複双直交変換

川田、池原 雅章

電子情報通信学会論文誌A, Vol.J-85-A （ No.3 ） 282-289 2002年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

１次元ラティス構造に基づく２次元フィルタバンクの設計

五十君、堀部、池原 雅章

電子情報通信学会論文誌A, Vol.J-84-A , pp. （ No.8 ） 999-1006 2001年08月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

IIRフィルタを用いた可逆及び非可逆変換と画像符号化への応用

宇戸、河野、奥田、池原 雅章、高橋

電子情報通信学会論文誌A, Vol.J-84-A （ No.7 ） 893-900 2001年07月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Lattice structures parameterization of linear phase paraunitary matrices with pairwise mirror-image symmetry in the frequency domain with an odd number of rows

C.W.Kok, T.Nagai, M.Ikehara, T.Q.Nguyen

IEEE Trans. On Circuits and Systems II, Vol.48 . （ No. 6 ） 633 -636 2001年06月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

リンギング抑制のためのフィルタバンクの構成

浅野、竹内、池原 雅章

電子情報通信学会論文誌A, Vol.J-84-A （ No.5 ） 576-585 2001年05月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Filter Banks with Nonlinear Lifting Steps for Lossless Image Compression

M.Okuda, S.K.Mitra, M. Ikehara, S. Takahashi

IEICE Trans. Fundamental, volE84-A （ No.13 ） 797-801 2001年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Generalized Unequal Length Lapped Orthogonal Transform for subband Image Coding

T.Nagai, M.Ikehara,Kaneko, Kurematsu

IEEE Trans. on Signal Processing vol.48 （ No.12 ） 3365-3378 2000年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Design and factorization of FIR paraunitary filter banks given several analysis filters

Kok, C.W.; Ikehara, M.; Nguyen, T.Q.

IEEE Trans. on Signal Processing vol.48 （ No.7 ） 2157-2161 2000年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Time-domain design and lattice structure of FIR paraunitary filter banks with linear phase

M.Ikehara, T.Nagai, T.Nguyen

Signal Processing, vol.80   333-342 2000年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

準最適な符号化利得を持つ線形位相パラユニタリフィルタバンクの設計

池原、T.Nguyen

電子情報通信学会論文誌A ,1999 Vol.J-82-A （ No.11 ） 1696-1703 1999年11月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Linear phase paraunitary filter bank with filters of different lengths and its application in image compression

T.D.Tran, M.Ikehara, T.Q. Nguyen

IEEE Trans. on Signal Processing, vol.47 （ No.10 ） 2730-2744 1999年10月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

所望応答の変換によるFIRディジタルフィルタの複素領域における準等リップル近似

奥田、清瀬、池原、高橋

電子情報通信学会論文誌A,1999 Vol.J-82-A （ No.8 ） 1312-1319 1999年08月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

不等分割直交変換の直接構成

長井、池原

電子情報通信学会論文誌A,1999 Vol.J82-A （ No.7 ）  1999年07月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Fast LOT and Unequal Lemgth Basis Functions: Realization and Application in Subband Image Coding

T.Nagai, M.Ikehara

IEICE Trans. Fundamental,1999 vol. E82-A （ No.5 ）  1999年05月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Two-Dimensional Perfect Reconstruction FIR Filter Banks with Triangular Supports

IEEE Trans. on Signal Processing vol.47 （ No.4 ） 1003-1009 1999年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Design of Biorthogonal Filter Banks Composed of Linear Phase IIR Filters

IEICE Trans. Fundamental volE81-A （ No.11 ）  1998年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

線形位相を有する完全再構成不等分割フィルタバンク

長井、淵江、池原

電子情報通信学会論文誌A vol.J81-A （ No.6 ） 916-927 1998年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

線形位相を有する完全再構成不等分割フィルタバンクの理論と設計法を示した。

Fast and Stable Least Squares Approach for the Design of Linear Phase FIR Filters

M. Okuda, M. Ikehara, S. Takahashi

IEEE Trans. on Signal Processing Vol.46 （ No.6 ） 1485-1493 1998年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

線形位相FIRフィルタの高速かつ安定な最小二乗法を提案した。

線形位相双直交フィルタバンクの新しい設計アプローチ

長井、池原

電子情報通信学会論文誌A vol.J81-A （ No.4 ） 613-621 1998年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

線形位相双直交フィルタバンクの新しい設計法を提案した。

Design of Linear Phase M-channel Perfect Reconstruction FIR Filter Bnaks

T.Nagai, T.Fuchie, M. Ikehara

IEEE Trans. on Signal Processing vol.45 （ No.9 ） 2380-2386 1997年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

リフティングによる線形位相Mチャンネル完全再構成FIRフィルタバンクの設計法を提案した。

誤差応答の変換による等リップルFIRディジタルフィルタの解析的設計法

桜庭、池原

電子情報通信学会論文誌A Vol.J-80-A （ No.10 ） 1672-1680 1997年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

誤差応答の変換による等リップルFIRディジタルフィルタの解析的設計法をしました。

Design of Lattice Structure of FIR Paraunitary Filter Banks with Linear Phase

T.Nagai, C.W.Kok, M.Ikehara, T.Q.Nguyen

IEICE Trans. Fundamental volE80-A （ No.4 ）  1997年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

線形位相FIRパラユニタリフィルタバンクのラティス構造を示した。

IIRディジタルフィルタの最小２乗法と等リップル解への応用

奥田、池原、高橋

電子情報通信学会論文誌A Vol.J-80-A （ No.4 ）  1997年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

IIRディジタルフィルタの最小２乗法を示し，等リップル解を得る方法を示した。

２次元FIRディジタルフィルタのための逆行列演算を含まない最小２乗法

奥田、池原、高橋

電子情報通信学会論文誌A Vol.J-80-A （ No.3 ） 463-471 1997年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

２次元FIRディジタルフィルタのための逆行列演算を含まない最小２乗法を提案した。

因果性を満たす２分割完全再構成IIRフィルタバンクの設計

奥田、池原、高橋

電子情報通信学会論文誌A Vol.J-80-A （ No.3 ） 454-462 1997年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

因果性を満たす２分割完全再構成IIRフィルタバンクの設計法を提案した。

フラクタル符号化の高速化

富樫、池原、野寺

情報処理学会論文誌 vol.38 （ No.1 ） 64-72 1997年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

フラクタル圧縮における高速符号化法を提案した。

コサイン変調2次元時変フィルタバンク

今田、池原 雅章、高橋

電子情報通信学会論文誌A, Vol.J79-A （ No.12, ） 1977-1985 1996年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

線形位相完全再構成不等分割FIRフィルタバンクの設計

渕江長井、池原

電子情報通信学会論文誌A Vol.J79-A （ No.12 ） 1957-1965 1996年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

線形位相完全再構成不等分割FIRフィルタバンクの設計法を提案した。

Simultaneous Approximation for IIR Digital Filters with Log Magnitude and Phase Response

奥田正浩、池原 雅章、高橋進一

IEICE Trans. Fundamental Vol.E79-A （ No.11 ） 1879-1885 1996年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

非最大間引き完全再構成FIRフィルタバンクの設計

長井、池原

電子情報通信学会論文誌A Vol.J79-A （ No.10 ） 1672-1680 1996年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

非最大間引き完全再構成FIRフィルタバンクの設計法を提案した。

DFT変調による完全再構成FIRフィルタバンクの設計

長井、筒井、池原

電子情報通信学会論文誌A Vol.J79-A （ No.6 ） 1165-1172 1996年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

DFT変調による完全再構成FIRフィルタバンクの設計法を提案した。

直線位相を持つコサイン変調２次元完全再構成FIRフィルタバンク

長井、池原

電子情報通信学会論文誌A Vol.J79-A （ No.3 ） 590-598 1996年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

直線位相を持つコサイン変調２次元完全再構成FIRフィルタバンクの構成法を提案した。

２次元フィルタを用いたMaximally Decimated１次元不等分割フィルタバンクの実現法

福岡、小澤、池原、高橋

電子情報通信学会論文誌A Vol.J78-A （ No.11 ） 1451-1459 1995年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

２次元フィルタををスライスすることにより，１つの回路でMaximally Decimated１次元不等分割フィルタバンクを実現する方法を提案した。

Lagrange-Newton法を用いた線形位相M分割全再構成FIRフィルタの設計

長井、池原

電子情報通信学会論文誌A Vol.J78-A （ No.9 ）  1995年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

Lagrange-Newton法を用いた線形位相M分割全再構成FIRフィルタの設計法を提案した。

重みつき最小自乗法を用いた全域通過回路の群遅延特性近似

奥田、松山、池原

電子情報通信学会論文誌A Vol.J78-A （ No.9 ）  1995年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

重み付き最小二乗法を用いて，全域通過回路の群遅延近似の手法を提案した。

A Design Method of All-pass networks Based on the Eigen Filter Method with Consideration of the Stability

Y. Toguri, M. Ikehara

IEICE Trans. Fundamental vol.E78-A （ No.7 ）  1995年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

安定性を考慮した固有値フィルタに基づく全域通過回路の設計法を提案した。

IIRディジタルフィルタの複素チェビシェフ近似

奥田、池原、高橋

電子情報通信学会論文誌A 1995年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

IIRフィルタにRemezアルゴリズムを適用し，複素誤差の絶対値が等リップルになるIIRディジタルフィルタの設計法を提案した。

阻止域等リップル特性を持つ線形位相２分割完全再構成FIRフィルタバンクの設計と実現

長井、池原

電子情報通信学会論文誌A 1995年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

阻止域等リップル特性を持つ線形位相双直交２分割完全再構成FIRフィルタバンクの設計と実現法を提案した。

任意のレギュラリティを持つ線形位相直交ウエーブレット関数

安岡寛道、坂本裕司、池原雅章

電子情報通信学会論文誌 J77-A-12 1994年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

任意のレギュラリティを有する線形位相直交ウエーブレット関数を導出した。

再帰形直交ウエーブレット関数

安岡寛道、池原雅章、磯部広幸

電子情報通信学会論文誌 Vol. J77-A-9   1231?1240 1994年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

全域通過回路の並列構造を用いて,完全再構成条件を見たし,レギュラリティヲ付加することにより,再帰型直交ウエーブレット関数を導出した。

コサイン変調２次元完全再構成フィルタバンクの理論

池原雅章

電子情報通信学会論文誌 Vol. J77-A-8   1110?1117 1994年08月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

　概要を見る

全域通過回路からなる２次元N-thバンドフィルタをコサインで変調することにより，２次元完全再構成フィルタバンクの理論を示した。

Design of IIR digital filters using all-pass networks

Tanaka, M.Ikehara, H. Kuroda

IEEE Trans. on Circuits and Systems, Vol. 41 （ No. 3 ） 231〜235 1994年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

通過域・阻止域で低係数感度なIIRディジタルフィルタ

戸栗康裕、池原雅章

電子情報通信学会論文誌 Vol. J77-A （ No. 3 ） 561?564 1994年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

全域通過回路の並列構造は，通過域で低係数感度であることが知られているが，これを縦続接続することにより,通過域，阻止域両域において低感度なディジタルフィルタを示した。

任意のサンプリング格子を持つ２次元完全再構成フィルタバンクの設計 -２次元Nth－bandディジタルフィルタの設計-

池原雅章、澤田清史、磯部広幸、山下明延、黒田英夫

電子情報通信学会論文誌A Vol. J76-A   933?941 1993年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

全域通過回路の並列構造を用いて，任意のサンプリング講師に対する２次元N-thバンドフィルタの構成法を提案した。

Design of Complex All-pass Networks Using Remez Algorithm

池原雅章、船石昌朋、黒田英夫

IEEE Transactions on Circuits and Systems-II Vol. 39 （ No.8 ） 549?556 1993年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

Remezアルゴリズムを用いた全域通過回路の最適設計法を提案した。

可変速度動画像符号化方式

池原雅章、黒田英夫

電子情報通信学会論文誌B VOL. J75-B-I （ No. 8 ） 536?544 1992年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

画像を優先領域と，非優先領域に分け，それぞれを効率的に符号化する方式を提案した。

２分割完全QMFの設計

池原雅章

電子情報通信学会論文誌A Vol. J75-A （ No.8 ） 1333?1340 1992年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

全域通過回路の並列構造を用いて，構造的に完全再構成を有する２分割QMFの設計法を提案した。

全域通過回路を用いた直線位相２次元ハーフバンドIIRフィルタの設計

池原雅章、田中宏幸

電子情報通信学会論文誌A VOL. J75-A （ No.7 ） 1164?1170 1992年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

全域通過回路と遅延器の並列構造を用いて，構造的に線形位相を有するIIRディジタルフィルタの設計法を提案した。

全域通過回路を用いたIIRディジタルフィルタの設計 -Remezアルゴリズムによる方法-

池原雅章，田中宏幸

電子情報通信学会論文誌A Vol.J75-A （ No.4 ） 710?717 1992年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

全域通過回路の並列構造を使って，RemezアルゴリズムによるIIRディジタルフィルタの最適設計法を提案した。

新しい２次元QMFの設計

電子情報通信学会論文誌(A) 1992年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

折り返し歪、位相歪、振幅歪を完全に除去可能な新しい因果的、及び非因果的２次元qmfの実現法を提案した。

Remezアルゴリズムを用いたディジタル全域通過回路の設計

電子情報通信学会論文誌(A) Vol. J74-A   974?979 1991年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

位相誤差を振幅誤差に変換し、これにRemezアルゴリズムを適用し、最適な全域通過回路の設計法を示した。

Remezアルゴリズムを用いたIIRヒルベルト変換器の設計

電子情報通信学会論文誌(A) Vol.J4-A   414?420 1991年

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

Remezアルゴリズムを用いて、IIRハーフバンドフィルタを設計し、これを周波数変換することにより、最適な因果的IIRヒルベルト変換器の設計法を示した。

アナログフィルタ理論に基づくIIRディジタル直交ミラーフィルタの設計

電子情報通信学会論文誌(A) Vol.JB-A   801?808 1990年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

アナログフィルタ理論から２つの全域通過関数に分離可能な伝達関数を導出し、これを双一次変換することにより、容易に２つの全域通過関数に分離できることを示した。

QMFフィルタバンクにおける全フィルタ係数の簡単な決定法

電子情報通信学会論文誌(A) Vol.J73-A   801?808 1990年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

基本となるディジタルフィルタから、簡単な変数変換によりＱＭＦフィルタバンクの全フィルタ係数が決定できることを示した。

全域通過サブフィルタを構成要素とする２次元ディジタルフィルタ

電子情報通信学会論文誌(A) Vol..J71-A   909?915 1989年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

遅延器と２次元全域通過回路を並列接続し、近似的線形位相を有し、かつ低係数感度特性を持つ円対称低域通過フィルタの設計法を示した。

インターポレーション技法を用いた広帯域低域通過フィルタの設計

電子情報通信学会論文誌（A) Vol.J71-A   2075?2077 1988年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

インターポーレーション技法を用いて、乗算器数低減を目的とした広帯域低減通過フィルタの新しい設計法を示した。

タップ切り換えに基づく遮断周波数可変形ディジタルフィルタ

電子情報通信学会論文誌（A） Vol.J71-A   2009?2017 1988年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

FIR形低域通過フィルタのインパルス応答列から、Ｍ番目ごとのインパルス列を取り出すことにより、元のＭ倍の帯域幅を持つ低域通過フィルタが得られることを示した。

プリフィルタを含む２次元フィルタバンク

電子情報通信学会論文誌（A） Vol.J71-A   1852?1860 1988年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

IFIR形伝達関数を持つ２次元プリフィルタと２次元DFTポリフェーズフィルタバンクを用いた新しい２次元フィルタバンクの構成法を示した。

インターポレーション技法を用いた狭帯域フィルタの設計

電子情報通信学会論文誌（A） Vol.J71-A   1673?1681 1988年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

IFIRフィルタという少量の乗算器で急峻な特性を実現できるフィルタを用いて、狭帯域特性を有するディジタルフィルタの設計法を示した。

解析信号変換器の設計－ＩＩＲ及びフィルタバンク形－

電子情報通信学会論文誌（A） Vol.J71-A   677?685 1988年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

解析信号変換器の設計法として、ｚ２を変数とする低域通過フィルタから導出する方法と、フィルタバンクを用いて実現する方法の２つの方法を示した。

N-pathディジタルフィルタを含む等分割フィルタバンク

電子情報通信学会論文誌（A） Vol.J70-A   1739?1748 1987年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

Ｎパスフィルタを用いた等分割フィルタバンクの構成法を示し、これを用いた低域通過フィルタの設計法を示す。

任意の通過帯域を持つマルチレートFIRディジタルフィルタの設計

電子情報通信学会論文誌（A） Vol.J70-A   1793?1802 1987年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

実係数マルチレート帯域通過ディジタルフィルタの構成法を示し、この補特性を用いて任意の通過帯域を持つマルチレート低減通過フィルタの設計法を示した。

マルチレートディジタルフィルタ

池原　雅章、高橋　進一

電子情報通信学会論文誌 Vol.70   1038-1041 1987年10月

研究論文（学術雑誌）, 共著

　概要を見る

マルチレートディジタルフィルタの研究の動向及び今後の展望を概説した。

複素係数を持つマルチレート帯域通過ディジタルフィルタの設計

電子通信学会論文誌（A） Vol.J69-A   950?957 1986年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

低域通過マルチレートディジタルフィルタに、複素係数化の手法を導入し、複素係数を持つマルチレート帯域通過ディジタルフィルタの構成法を示した。

一つの演算増幅器と最小個数の等価素子を用いたスイッチトキャパシタフィルタ

電子通信学会論文誌（A） Vol. J68-A   161?165 1985年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

　概要を見る

一つの演算増幅器と最小個数の等価素子だけを用いたスイッチトキャパシタフィルタの構成法を示した。