Paper Abstract and Keywords |
Presentation |
2015-03-06 11:05
A Study on Learning-Based Super-Resolution By Sparse Coding Using Image Search Based on SSIM Kodai Azuma, Masayuki Kurosaki, Hiroshi Ochi (KIT) SIS2014-107 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Super-Resolution is one of the methods used to enhance image resolution.
Up until now, an Learning-Based Super-Resolution method that utilizes high resolution image and low resolution image pairings in order to compensate high frequency component has been proposed.
In this conventional method, similarity patch is retrieved by using PSNR and differential filter.
However, PSNR can only evaluate the average error in brightness while differential filter can only evaluate the difference. Thus, it tends to search for images that are not similar to the structure of the given image.
Particularly in Learning-Based Super-Resolution using Sparse Coding, considering feature value as the basis, it is important to search for patches that preserve the most suitable image structure in order to restore the image.
In this study, we propose an image searching method using SSIM that considers the image’s average brightness, contrast, and similarities in image structure.
We show the effectiveness of the proposed method over the conventional method from the results after the proposed super resolution method is conducted.
end{eabstract} |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Sparse Coding / Learning-Based Super-Resolution / SSIM / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 114, no. 496, SIS2014-107, pp. 91-95, March 2015. |
Paper # |
SIS2014-107 |
Date of Issue |
2015-02-26 (SIS) |
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) |
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SIS2014-107 |
Conference Information |
Committee |
SIS |
Conference Date |
2015-03-05 - 2015-03-06 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Meiji Univ. Nakano Campus (Tokyo) |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Soft Computing, etc. |
Paper Information |
Registration To |
SIS |
Conference Code |
2015-03-SIS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Learning-Based Super-Resolution By Sparse Coding Using Image Search Based on SSIM |
Sub Title (in English) |
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Sparse Coding |
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Learning-Based Super-Resolution |
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SSIM |
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1st Author's Name |
Kodai Azuma |
1st Author's Affiliation |
Kyushu Institute of Technology (KIT) |
2nd Author's Name |
Masayuki Kurosaki |
2nd Author's Affiliation |
Kyushu Institute of Technology (KIT) |
3rd Author's Name |
Hiroshi Ochi |
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Kyushu Institute of Technology (KIT) |
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Speaker |
Author-1 |
Date Time |
2015-03-06 11:05:00 |
Presentation Time |
20 minutes |
Registration for |
SIS |
Paper # |
SIS2014-107 |
Volume (vol) |
vol.114 |
Number (no) |
no.496 |
Page |
pp.91-95 |
#Pages |
5 |
Date of Issue |
2015-02-26 (SIS) |
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