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Paper Abstract and Keywords
Presentation 2022-06-09 13:25
Learning Method for Image Denoising by Weighted Sum of Perceptual Quality Assessment Methods
Takamichi Miyata (Chiba Inst. Tech.) NLP2022-2 CCS2022-2
Abstract (in Japanese) (See Japanese page) 
(in English) Existing deep learning-based denoising methods employ mean squared error (MSE) as a loss function. As a result, the output image is excessively smoothed and has low perceptual quality. On the other hand, image quality assessment (IQA), which uses deep learning to estimate the perceptual quality of images, has been proposed. However, existing studies have reported that when such IQA is used alone as a loss function in denoising methods, not only is the signal quality significantly degraded, but also the perceptual quality is not improved. This is most likely due to the presence of certain images in each IQA that cause the IQA in question to malfunction. To avoid the aforementioned problem and improve the perceptual quality of denoised images, we propose a method for learning denoising methods using a loss function that combines IQA with other IQA or MSE. To confirm the effectiveness of the proposed method, we qualitatively and quantitatively compared the denoising performance of the proposed method with that of comparative methods. The results show that the proposed method reduces excessive smoothing of the image and improves the perceived quality by strongly adding texture.
Keyword (in Japanese) (See Japanese page) 
(in English) Image denoising / deep learning / perceptual quality / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 66, CCS2022-2, pp. 7-12, June 2022.
Paper # CCS2022-2 
Date of Issue 2022-06-02 (NLP, CCS) 
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 NLP2022-2 CCS2022-2

Conference Information
Committee CCS NLP  
Conference Date 2022-06-09 - 2022-06-10 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To CCS 
Conference Code 2022-06-CCS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Learning Method for Image Denoising by Weighted Sum of Perceptual Quality Assessment Methods 
Sub Title (in English)  
Keyword(1) Image denoising  
Keyword(2) deep learning  
Keyword(3) perceptual quality  
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1st Author's Name Takamichi Miyata  
1st Author's Affiliation Chiba Institute of Technology (Chiba Inst. Tech.)
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Speaker Author-1 
Date Time 2022-06-09 13:25:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # NLP2022-2, CCS2022-2 
Volume (vol) vol.122 
Number (no) no.65(NLP), no.66(CCS) 
Page pp.7-12 
#Pages 6 
Date of Issue 2022-06-02 (NLP, CCS) 

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