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Paper Abstract and Keywords
Presentation 2026-03-05 12:30
Memory-Efficient Diffusion-based Image Denoising Networks with Multi-Scale Structures
Shugo Ito, Makoto Nakashizuka (CIT) SIS2025-57
Abstract (in Japanese) (See Japanese page) 
(in English) In the field of image processing, methods based on deep neural network expansion have been proposed to achieve noise removal and image interpolation with a small number of parameters. One such method is a noise removal network that uses a deep neural network expansion of a diffusion equation incorporating nonlinearity. In this method, noise is estimated in one network layer by combining Laplacian components and nonlinear functions, and then subtracted from the image. By multiplying this process, Gaussian noise removal is achieved. In this study, we propose a network configuration using a Trous structurecite{Trous} to improve the accuracy of noise estimation while suppressing the increase in the number of parameters. Furthermore, by introducing rotation into the filter kernel, we achieved noise removal with four times the number of filters for the same number of parameters. In experiments, compared to DnCNN, a deep learning-based noise removal method, the number of parameters could be reduced to approximately 1/80, although the restoration accuracy was approximately 0.1 to 0.3 dB lower in PSNR.
Keyword (in Japanese) (See Japanese page) 
(in English) Image Processing / deep learning / deep unrolling / diffusion process / denoising / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 393, SIS2025-57, pp. 1-5, March 2026.
Paper # SIS2025-57 
Date of Issue 2026-02-26 (SIS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF SIS2025-57

Conference Information
Committee SIS  
Conference Date 2026-03-05 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Ohkubo Campus, Saitama University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIS 
Conference Code 2026-03-SIS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Memory-Efficient Diffusion-based Image Denoising Networks with Multi-Scale Structures 
Sub Title (in English)  
Keyword(1) Image Processing  
Keyword(2) deep learning  
Keyword(3) deep unrolling  
Keyword(4) diffusion process  
Keyword(5) denoising  
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1st Author's Name Shugo Ito  
1st Author's Affiliation Chiba Institute of Technology (CIT)
2nd Author's Name Makoto Nakashizuka  
2nd Author's Affiliation Chiba Institute of Technology (CIT)
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Speaker Author-1 
Date Time 2026-03-05 12:30:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2025-57 
Volume (vol) vol.125 
Number (no) no.393 
Page pp.1-5 
#Pages 5 
Date of Issue 2026-02-26 (SIS) 

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