Details of a Researcher - Ikehara, Masaaki

Ikehara, Masaaki

写真a

Affiliation: Faculty of Science and Technology, Department of Electronics and Electrical Engineering (Yagami)

Position: Professor

Related Websites

http://www.tkhm.elec.keio.ac.jp

External Links

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Career 【 Display / hide 】

1989.04

-

1992.03

長崎大学工学部 ,専任講師
1992.04

-

1996.03

慶應義塾大学理工学部電気工学科 ,専任講師
1996.04

-

1998.03

慶應義塾大学理工学部電子工学科兼電気工学科 ,専任講師
1998.04

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Present

慶應義塾大学理工学部 ,助教授

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Academic Background 【 Display / hide 】

1984.03

Keio University, Faculty of Engineering, 電気工学科

University, Graduated
1986.03

Keio University, Graduate School, Division of Science and Engineeri, 電気工学専攻

Graduate School, Completed, Master's course
1989.03

Keio University, Graduate School, Division of Science and Engineeri, 電気工学専攻

Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

工学　, Keio University, 1989.03

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Research Areas 【 Display / hide 】

Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Communication and network engineering (Digital Signal Processing)

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Books 【 Display / hide 】

MATLABマルティメディア信号処理（下）

IKEHARA MASAAKI, 培風館, 2005
培風館

IKEHARA MASAAKISHIMAMURA TETSUYA, 2004.01
培風館

池原雅章、真田幸俊, 2002.02
科学技術出版

池原雅章, 2001.09
培風館

高橋、池原, 培風館, 1999.07

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Papers 【 Display / hide 】

Efficient and Effective Blind JPEG Image Improvement With Sequential Feature Processing

Ezumi S., Ikehara M.

IEEE Access 12 151975 - 151986 2024.10

Research paper (scientific journal), Accepted

　View Summary

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.
- Access to Document (DOI)
Edge-Guided Low-Light Image Enhancement Based on GAN with Effective Modules

Matsui T., Ikehara M.

European Signal Processing Conference 456 - 460 2024.09

Research paper (international conference proceedings), Accepted, ISSN 22195491

　View Summary

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.
- Access to Document (DOI)
MSARNet: Efficient JPEG Artifact Removal Using Multi-Stage Style Network

Ezumi S., Ikehara M.

Digest of Technical Papers - IEEE International Conference on Consumer Electronics 2024.01

Research paper (international conference proceedings), Accepted, ISSN 0747668X

　View Summary

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.
- Access to Document (DOI)
Multiple Adverse Weather Removal Using Masked-Based Pre-Training and Dual-Pooling Adaptive Convolution

Yamashita S., Ikehara M.

IEEE Access 12 58057 - 58066 2024

Research paper (scientific journal)

　View Summary

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.
- Access to Document (DOI)
Faster Training of Large Kernel Convolutions on Smaller Spatial Scales

Fukuzaki S., Ikehara M.

IEEE Access 12 161312 - 161328 2024

Research paper (scientific journal)

　View Summary

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.
- Access to Document (DOI)

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Papers, etc., Registered in KOARA 【 Display / hide 】

Research on rapid and accurate image restoration by fusion of signal processing and deep learning

Ikehara, Masaaki

科学研究費補助金研究成果報告書 2022
High accuracy position and pose estimation for object recognition using phase information

Ikehara, Masaaki

科学研究費補助金研究成果報告書 2019
Image super-resolution using similarity

Ikehara, Masaaki

科学研究費補助金研究成果報告書 2015
Design of 2D directional adaptive filter banks and its application to image coding

Ikehara, Masaaki

科学研究費補助金研究成果報告書 2011
Lifting Structure of M-channel Filter Banks and its Application to Lossy-Lossless Coding

IKEHARA, MASAAKI

科学研究費補助金研究成果報告書 2008

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Presentations 【 Display / hide 】

Image Rrestoration based on Weighted Average of Multiple Blurred and Noisy Images

Ryo Tanikawa, Takanori Fujisawa, Masaaki Ikehara

2018 International Workshop on Advanced Image Technology (IWAIT 2018),

2018.01
,
Oral presentation (general)
Noise Removal based on Surface Approximation of Color Line

Koichi Manabe Takuro Yamaguchi and Masaaki Ikehara

2018 International Workshop on Advanced Image Technology (IWAIT 2018),

2018.01
,
Oral presentation (general)
Random-valued Impulse Noise Removal Using Non-local Search for Similar Structures and Sparse Representation

Kengo Tsuda, Takanori Fujisawa, Masaaki Ikehara

2018 International Workshop on Advanced Image Technology (IWAIT 2018),

2018.01
,
Oral presentation (general)
Joint Bilateral based Image Denoising using Multi-sized 2D Hard Threshold

Yamaguchi Takuro and IKEHARA MASAAKI

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 2017 (Kuala Lumpur) ,

2017.12
,
Oral presentation (general)
Color Image Coding based on Linear Combination of Adaptive Colorspaces

Takanori Fujisawa and Masaaki Ikehara

42nd IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2017),

2017.03
,
Oral presentation (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

Degradation removal of images by nonlocal deep learning using signal processing techniques

2023.04

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2026.03

基盤研究(C), Principal investigator
Research on rapid and accurate image restoration by fusion of signal processing and deep learning

2020.04

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2023.03

MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator
High accuracy position and pose estimation for object recognition using phase information

2017.04

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2020.03

MEXT,JSPS, Grant-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator