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| Presentation | 2019-03-14 13:30 [Poster Presentation] Image Super-Resolution via Generative Adversarial Network Considering Objective Quality Hiroya Yamamoto, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.)  EA2018-115 SIP2018-121 SP2018-77 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose a super-resolution method based on a conventional technique using the generative adversarial network (GAN). The conventional method suffers from pixel-wise black or white artifacts. This is because the number of training data was not sufficient for a network with huge parameters. To solve this problem, we exploit the observation error by adding it to the conventional cost function. This guarantees the consistency for training examples, but not for a novel input data after training. Hence, we further introduce the orthogonal projection onto a linear manifold, in which the observation error is completely zero. These two ideas enable us to produce high quality super-resolution results without the artifact. Simulation results show the effectiveness of the proposed method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Image Super-Resolution / Generative Adversarial Network / Orthogonal Projection / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 496, SIP2018-121, pp. 93-98, March 2019. | |
| Paper # | SIP2018-121 | |
| Date of Issue | 2019-03-07 (EA, SIP, SP) | |
| 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 | EA2018-115 SIP2018-121 SP2018-77 |
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| Conference Information | |
| Committee | EA SIP SP |
| Conference Date | 2019-03-14 - 2019-03-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | i+Land nagasaki (Nagasaki-shi) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Engineering/Electro Acoustics, Signal Processing, Speech, and Related Topics |
| Paper Information | |
| Registration To | SIP |
| Conference Code | 2019-03-EA-SIP-SP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Image Super-Resolution via Generative Adversarial Network Considering Objective Quality |
| Sub Title (in English) | |
| Keyword(1) | Image Super-Resolution |
| Keyword(2) | Generative Adversarial Network |
| Keyword(3) | Orthogonal Projection |
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| 1st Author's Name | Hiroya Yamamoto |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Daichi Kitahara |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 3rd Author's Name | Akira Hirabayashi |
| 3rd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-03-14 13:30:00 |
| Presentation Time | 90 minutes |
| Registration for | SIP |
| Paper # | EA2018-115, SIP2018-121, SP2018-77 |
| Volume (vol) | vol.118 |
| Number (no) | no.495(EA), no.496(SIP), no.497(SP) |
| Page | pp.93-98 |
| #Pages | 6 |
| Date of Issue | 2019-03-07 (EA, SIP, SP) |
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