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| Presentation | 2021-11-25 10:25 wganBCS: Block-wise image compressive sensing and reconstruction model using adversarial training to eliminate block effects Boyan Chen (Hosei Univ./NPU), Kaoru Uchida (Hosei Univ.)  CS2021-60 IE2021-19 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The famous block-wise compressive sensing (BCS) paradigm can greatly reduce the memory consumption of sensing matrix compared with traditional image compressive methods. However, BCS paradigm still suffers from two issues. One is that block-wise sensing causes heavy block effect on the reconstructed image, which leads to degradation in the image quality metrics. Another is that the sate of art block wise image compressive sensing methods only use mean square error loss function to optimize their models, which causes the reconstructed images over smoothed. In this paper, we incorporate generative adversarial training into BCS paradigm and propose a new block wise image compressive sensing and reconstruction model called wganBCS, which a combination of traditional L2 loss and the wasserstein loss are used to optimize the model. We propose a modified wasserstein GAN (WGAN) network to deal with the block effect caused by the block wise compressive sensing. Specifically speaking, the generator network will minimize the wasserstein distance calculated by the critic network to keep the reconstructed images visually authentic to ground truth images. Experimental result shows that our model is superior both in visual authenticity and the image quality metrics compared to most state of art image compressive sensing methods. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Image compressed sensing / Generative adversarial Networks / Deep Learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 269, IE2021-19, pp. 1-6, Nov. 2021. | |
| Paper # | IE2021-19 | |
| Date of Issue | 2021-11-18 (CS, IE) | |
| ISSN | 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 | CS2021-60 IE2021-19 |
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| Conference Information | |
| Committee | IPSJ-AVM CS IE ITE-BCT |
| Conference Date | 2021-11-25 - 2021-11-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image coding, Communications and streaming technologies, etc. |
| Paper Information | |
| Registration To | IE |
| Conference Code | 2021-11-AVM-CS-IE-BCT |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | wganBCS: Block-wise image compressive sensing and reconstruction model using adversarial training to eliminate block effects |
| Sub Title (in English) | |
| Keyword(1) | Image compressed sensing |
| Keyword(2) | Generative adversarial Networks |
| Keyword(3) | Deep Learning |
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| 1st Author's Name | Boyan Chen |
| 1st Author's Affiliation | Hosei University/Northwestern Polytechnical University (Hosei Univ./NPU) |
| 2nd Author's Name | Kaoru Uchida |
| 2nd Author's Affiliation | Hosei University (Hosei Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-11-25 10:25:00 |
| Presentation Time | 25 minutes |
| Registration for | IE |
| Paper # | CS2021-60, IE2021-19 |
| Volume (vol) | vol.121 |
| Number (no) | no.268(CS), no.269(IE) |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2021-11-18 (CS, IE) |
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