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Paper Abstract and Keywords
Presentation 2020-12-18 14:50
Super resolution for sea surface temperature with CNN and GAN
Tomoki Izumi, Motoki Amagasaki, Kei Ishida, Masato Kiyama (Kumamoto Univ.) NC2020-28
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we use the deep neural networks (DNN)-based single image super-resolution (SISR) method for the super resolution of sea surface temperature data. By using state of the art DNN technology, we are able to generate high quality super-resolution data. In this evaluation, generated images are compared to OISST with the root mean square error (RMSE) and Learned Perceptual Image Patch Similarity (LPIPS) and Perceptual Index(PI). RRDBNet has a better RMSE than SRCNN and ESRGAN. On the other hand, CNN-based SISR model is not a faithful representation of the ocean currents of OISST. ESRGAN can represent the complex distribution of ocean currents.
Keyword (in Japanese) (See Japanese page) 
(in English) Single Image Super-Resolution / Convolutional Neural Network / Generative Adversarial Network / RRDBNet / ESRGAN / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 302, NC2020-28, pp. 1-6, Dec. 2020.
Paper # NC2020-28 
Date of Issue 2020-12-11 (NC) 
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 NC2020-28

Conference Information
Committee MBE NC  
Conference Date 2020-12-18 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NC 
Conference Code 2020-12-MBE-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Super resolution for sea surface temperature with CNN and GAN 
Sub Title (in English)  
Keyword(1) Single Image Super-Resolution  
Keyword(2) Convolutional Neural Network  
Keyword(3) Generative Adversarial Network  
Keyword(4) RRDBNet  
Keyword(5) ESRGAN  
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Keyword(7)  
Keyword(8)  
1st Author's Name Tomoki Izumi  
1st Author's Affiliation Kumamoto University (Kumamoto Univ.)
2nd Author's Name Motoki Amagasaki  
2nd Author's Affiliation Kumamoto University (Kumamoto Univ.)
3rd Author's Name Kei Ishida  
3rd Author's Affiliation Kumamoto University (Kumamoto Univ.)
4th Author's Name Masato Kiyama  
4th Author's Affiliation Kumamoto University (Kumamoto Univ.)
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Speaker Author-1 
Date Time 2020-12-18 14:50:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2020-28 
Volume (vol) vol.120 
Number (no) no.302 
Page pp.1-6 
#Pages 6 
Date of Issue 2020-12-11 (NC) 

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