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Paper Abstract and Keywords
Presentation 2014-03-14 09:00
Hierarchical Single Image Super-Resolution Based on Dictionary Learning
Toshihiro Kitao, Osamu Hasegawa (Tokyo Inst. of Tech.) PRMU2013-192
Abstract (in Japanese) (See Japanese page) 
(in English) Single image super-resolution, a technology to reconstruct a high-resolution image from a single low-resolution image, is expected of applications to resolution enhancement of already captured images, photographing high-quality images with inexpensive cameras, and so on. For single image super-resolution, learning-based super-resolution is known as an effective approach. In learning-based super-resolution, a high-resolution image is reconstructed in each small region of the image (patch). However, with large magnification factor, it is difficult to reconstruct a high-resolution image considering global image structure in existing learning-based super-resolution due to the local processing. In this paper, we propose hierarchical super-resolution that applies low magnification super-resolution repeatedly to obtain a high quality high-resolution image with large magnification factor.
Keyword (in Japanese) (See Japanese page) 
(in English) Single image super-resolution / Learning-besed super-resolution / Hierarchical super-resolution / large magnification factor / / / /  
Reference Info. IEICE Tech. Rep., vol. 113, no. 493, PRMU2013-192, pp. 145-150, March 2014.
Paper # PRMU2013-192 
Date of Issue 2014-03-06 (PRMU) 
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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Conference Information
Committee PRMU  
Conference Date 2014-03-13 - 2014-03-14 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2014-03-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Hierarchical Single Image Super-Resolution Based on Dictionary Learning 
Sub Title (in English)  
Keyword(1) Single image super-resolution  
Keyword(2) Learning-besed super-resolution  
Keyword(3) Hierarchical super-resolution  
Keyword(4) large magnification factor  
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1st Author's Name Toshihiro Kitao  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
2nd Author's Name Osamu Hasegawa  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Inst. of Tech.)
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Speaker Author-1 
Date Time 2014-03-14 09:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2013-192 
Volume (vol) vol.113 
Number (no) no.493 
Page pp.145-150 
#Pages 6 
Date of Issue 2014-03-06 (PRMU) 

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