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| Presentation | 2012-05-17 10:30 Image Super-Resolution using Manifold Learning with Vector Quantization Kazuki Taniguchi, Xian-Hua Han, Yutaro Iwamoto, So Sasatani, Yen-Wei Chen (Ritsumeikan Univ.)  IE2012-19 PRMU2012-4 MI2012-4 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Image Super-Resolution (SR) is to recover the lost high-frequency information from several or only one available image. Single-Frame SR, one of hot topics in SR research fields, can generate a high-resolution image from only one low-resolution image by using the prior prepared database. Therein, the example-based and neighborhood embedding-based SR are the very popular single-frame SRs to infer the lost information in the LR input with the known corresponding relations between LR and HR images in database which has to be prepared in large-scale for having most varieties of image, and then take a lot of computational time for inferring. Therefore, this study proposes to first obtain some prototypes from the prepared LR and HR images using vector quantization such as k-means clustering method, and the achieved prototypes are as the training database for inferring the lost information of any LR input. Then, the amount of corresponding LR and HR data in training database can be greatly reduced, which guarantee much less computational time. Experimental results also show that our proposed strategy can achieve higher quality high-resolution image and lower computational time than conventional methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Image Restoration / Super-Resolution / Manifold Learning / Vector Quantization / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 37, PRMU2012-4, pp. 19-24, May 2012. | |
| Paper # | PRMU2012-4 | |
| Date of Issue | 2012-05-10 (IE, PRMU, MI) | |
| 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 | IE2012-19 PRMU2012-4 MI2012-4 |
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| Conference Information | |
| Committee | PRMU MI IE |
| Conference Date | 2012-05-17 - 2012-05-18 |
| 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 | 2012-05-PRMU-MI-IE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Image Super-Resolution using Manifold Learning with Vector Quantization |
| Sub Title (in English) | |
| Keyword(1) | Image Restoration |
| Keyword(2) | Super-Resolution |
| Keyword(3) | Manifold Learning |
| Keyword(4) | Vector Quantization |
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| 1st Author's Name | Kazuki Taniguchi |
| 1st Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 2nd Author's Name | Xian-Hua Han |
| 2nd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 3rd Author's Name | Yutaro Iwamoto |
| 3rd Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 4th Author's Name | So Sasatani |
| 4th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
| 5th Author's Name | Yen-Wei Chen |
| 5th Author's Affiliation | Ritsumeikan University (Ritsumeikan Univ.) |
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| Speaker | Author-1 |
| Date Time | 2012-05-17 10:30:00 |
| Presentation Time | 30 minutes |
| Registration for | PRMU |
| Paper # | IE2012-19, PRMU2012-4, MI2012-4 |
| Volume (vol) | vol.112 |
| Number (no) | no.36(IE), no.37(PRMU), no.38(MI) |
| Page | pp.19-24 |
| #Pages | 6 |
| Date of Issue | 2012-05-10 (IE, PRMU, MI) |
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