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| Presentation | 2013-03-14 14:40 Self-learning super resolution by L2-norm Daiki Azuma (Keio Univ.), Kazu Mishiba (Tottori Univ.), Masaaki Ikehara (Keio Univ.)  CAS2012-106 SIP2012-137 CS2012-112 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we propose a single image super resolution technique by L2 approximation without any training. Recently it has been proposed that image super resolution by using sparse representation. However it has to make a dictionary from tremendous low and high resolution image pairs to apply in any kinds of inputs. Moreover this algorithm takes a long time to train and requires the dictionaries to learn in advance. In our method, the high- and low- resolution dictionaries are made from random selected patch pairs of input and its reduced image, respectively. Then the input image is approximated by low-resolution dictionary in the sense of L2 and its coefficients are used to reconstruct the high-resolution image. Therefore the proposed method does not need any training to make dictionaries and any time consuming computation for approximations. In some example, our method has comparable quality to the conventional method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | super resolution / sparse coding / l2-norm minimization / self-similarity / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 485, SIP2012-137, pp. 57-61, March 2013. | |
| Paper # | SIP2012-137 | |
| Date of Issue | 2013-03-07 (CAS, SIP, CS) | |
| 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 | CAS2012-106 SIP2012-137 CS2012-112 |
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| Conference Information | |
| Committee | SIP CAS CS |
| Conference Date | 2013-03-14 - 2013-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Keio Univ. Tsuruoka Campus (Yamagata) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network Processor, Signal Processing for communication, and Wireless LAN/PAN, etc. |
| Paper Information | |
| Registration To | SIP |
| Conference Code | 2013-03-SIP-CAS-CS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Self-learning super resolution by L2-norm |
| Sub Title (in English) | |
| Keyword(1) | super resolution |
| Keyword(2) | sparse coding |
| Keyword(3) | l2-norm minimization |
| Keyword(4) | self-similarity |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Daiki Azuma |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Kazu Mishiba |
| 2nd Author's Affiliation | Tottori University (Tottori Univ.) |
| 3rd Author's Name | Masaaki Ikehara |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2013-03-14 14:40:00 |
| Presentation Time | 25 minutes |
| Registration for | SIP |
| Paper # | CAS2012-106, SIP2012-137, CS2012-112 |
| Volume (vol) | vol.112 |
| Number (no) | no.484(CAS), no.485(SIP), no.486(CS) |
| Page | pp.57-61 |
| #Pages | 5 |
| Date of Issue | 2013-03-07 (CAS, SIP, CS) |
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