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Paper Abstract and Keywords
Presentation 2026-03-19 14:10
Enhancing Adaptability of Mixture-of-Complexity Experts Image Restoration to Clean Image Inputs
Yuma Watanabe, Seiichiro Imaizumi, Thanawit Gerdprasert, Toshikazu Samura (Yamaguchi Univ) MSS2025-73 NLP2025-154
Abstract (in Japanese) (See Japanese page) 
(in English) Mixture-of-Complexity Experts Image Restoration (MoCE-IR) dynamically selects experts with different computational costs according to input noise intensity. However, as the pretrained model is optimized only for noisy inputs, its performance often degrades when processing clean images. This study enhances the practical usability of pretrained MoCE-IR by introducing a plug-in skip mechanism. This mechanism identifies whether to bypass restoration processes based on the feature differences between the input and output of each decoder block. Experimental results demonstrate that the proposed plug-in mechanism preserves the quality of clean images while simultaneously reducing overall computational cost.
Keyword (in Japanese) (See Japanese page) 
(in English) Mixture-of-Complexity Experts for Image Restoration / Pretrained Model / Restoration Skipping / Plug-in / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 418, NLP2025-154, pp. 139-144, March 2026.
Paper # NLP2025-154 
Date of Issue 2026-03-11 (MSS, NLP) 
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 MSS2025-73 NLP2025-154

Conference Information
Committee MSS NLP  
Conference Date 2026-03-18 - 2026-03-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2026-03-MSS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancing Adaptability of Mixture-of-Complexity Experts Image Restoration to Clean Image Inputs 
Sub Title (in English)  
Keyword(1) Mixture-of-Complexity Experts for Image Restoration  
Keyword(2) Pretrained Model  
Keyword(3) Restoration Skipping  
Keyword(4) Plug-in  
Keyword(5)  
Keyword(6)  
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Keyword(8)  
1st Author's Name Yuma Watanabe  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ)
2nd Author's Name Seiichiro Imaizumi  
2nd Author's Affiliation Yamaguchi University (Yamaguchi Univ)
3rd Author's Name Thanawit Gerdprasert  
3rd Author's Affiliation Yamaguchi University (Yamaguchi Univ)
4th Author's Name Toshikazu Samura  
4th Author's Affiliation Yamaguchi University (Yamaguchi Univ)
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Speaker Author-1 
Date Time 2026-03-19 14:10:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2025-73, NLP2025-154 
Volume (vol) vol.125 
Number (no) no.417(MSS), no.418(NLP) 
Page pp.139-144 
#Pages 6 
Date of Issue 2026-03-11 (MSS, NLP) 

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