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Paper Abstract and Keywords
Presentation 2020-02-28 15:10
Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement
Satoshi Kosugi, Toshihiko Yamasaki (Univ. of Tokyo) ITS2019-52 IE2019-90
Abstract (in Japanese) (See Japanese page) 
(in English) This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance images utilizing image editing software to achieve noise-free, scale invariant, and interpretable image enhancement. To incorporate image editing software into a GAN, we propose a reinforcement learning framework. We apply the proposed method to photo enhancement and face beautification and demonstrate that the proposed method achieves the best performance.
Keyword (in Japanese) (See Japanese page) 
(in English) image enhancement / unpaired learning / reinforcement learning / generative adversarial network / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 422, IE2019-90, pp. 311-316, Feb. 2020.
Paper # IE2019-90 
Date of Issue 2020-02-20 (ITS, IE) 
ISSN Print edition: ISSN 0913-5685    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 ITS2019-52 IE2019-90

Conference Information
Committee ITE-HI IE ITS ITE-MMS ITE-ME ITE-AIT  
Conference Date 2020-02-27 - 2020-02-28 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image Processing, etc. 
Paper Information
Registration To IE 
Conference Code 2020-02-HI-IE-ITS-MMS-ME-AIT 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unpaired Learning for Noise-free, Scale Invariant, and Interpretable Image Enhancement 
Sub Title (in English)  
Keyword(1) image enhancement  
Keyword(2) unpaired learning  
Keyword(3) reinforcement learning  
Keyword(4) generative adversarial network  
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1st Author's Name Satoshi Kosugi  
1st Author's Affiliation the University of Tokyo (Univ. of Tokyo)
2nd Author's Name Toshihiko Yamasaki  
2nd Author's Affiliation the University of Tokyo (Univ. of Tokyo)
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Speaker Author-1 
Date Time 2020-02-28 15:10:00 
Presentation Time 15 minutes 
Registration for IE 
Paper # ITS2019-52, IE2019-90 
Volume (vol) vol.119 
Number (no) no.421(ITS), no.422(IE) 
Page pp.311-316 
#Pages 6 
Date of Issue 2020-02-20 (ITS, IE) 

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