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| Paper Abstract and Keywords | ||
| Presentation | 2020-07-16 14:20 A Study on Trainable ISTA using Auto Encoder as Shrinkage Function for Image Recovery Kento Yokoyama, Satoshi Takabe, Tadashi Wadayama (NIT)  IT2020-13 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | ISTA (Iterative Shrinkage-Thresholding Algorithm) is one of the basic algorithms used in compressed sensing to estimate sparse signals from observed signals. Recently, techniques such as Trainable ISTA that combine deep learning with compressed sensing algorithms have achieved high signal recovery performance. In this paper, we propose the DAE-ISTA that uses a DAE (Denoising AutoEncoder) with pre-trained image features as the shrinkage function of ISTA for image recovery. Numerical simulations based on real data have revealed that DAE-ISTA improves the image recovery performance compared with an existing method. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | DAE / ISTA / compressed sensing / deep learning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 105, IT2020-13, pp. 13-18, July 2020. | |
| Paper # | IT2020-13 | |
| Date of Issue | 2020-07-09 (IT) | |
| 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 | IT2020-13 |
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| Conference Information | |
| Committee | IT |
| Conference Date | 2020-07-16 - 2020-07-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Freshman session, General |
| Paper Information | |
| Registration To | IT |
| Conference Code | 2020-07-IT |
| Language | Japanese without English title) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Trainable ISTA using Auto Encoder as Shrinkage Function for Image Recovery |
| Sub Title (in English) | |
| Keyword(1) | DAE |
| Keyword(2) | ISTA |
| Keyword(3) | compressed sensing |
| Keyword(4) | deep learning |
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| 1st Author's Name | Kento Yokoyama |
| 1st Author's Affiliation | Nagoya Institute of Technology (NIT) |
| 2nd Author's Name | Satoshi Takabe |
| 2nd Author's Affiliation | Nagoya Institute of Technology (NIT) |
| 3rd Author's Name | Tadashi Wadayama |
| 3rd Author's Affiliation | Nagoya Institute of Technology (NIT) |
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| Speaker | Author-1 |
| Date Time | 2020-07-16 14:20:00 |
| Presentation Time | 25 minutes |
| Registration for | IT |
| Paper # | IT2020-13 |
| Volume (vol) | vol.120 |
| Number (no) | no.105 |
| Page | pp.13-18 |
| #Pages | 6 |
| Date of Issue | 2020-07-09 (IT) |
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