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| Presentation | 2023-03-02 10:25 Blind deconvolution with non-smooth regularization via Bregman proximal DC algorithms Shota Takahashi (SOKENDAI), Mirai Tanaka, Ikeda Shiro (ISM)  PRMU2022-79 IBISML2022-86 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Blind deconvolution is a technique to recover an original signal without knowing a convolving filter from its convolution. It is formulated as a minimization problem of a quartic loss function under some assumption. In this paper, we find a difference of convex functions (DC) decomposition for the loss function and apply the Bregman proximal DC algorithm (BPDCA) and the BPDCA with extrapolation (BPDCAe), which is an acceleration of BPDCA. Using this DC decomposition, we obtain the $L$-smooth adaptable ($L$-smad) property, which guarantees the global convergence of BPDCA. When our regularizer has a sufficiently simple structure, the subproblem of BPDCA(e) at each iteration is solved in a closed-form expression, and thus our algorithms solve large-scale problems efficiently. We also provide the stability analysis around the equilibrium point and demonstrate our proposed algorithms through numerical experiments on image deblurring. The results show that BPDCAe successfully recovered the original image and outperformed other existing algorithms. This paper is based on [21]. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Blind deconvolution / DC optimization / Bregman proximal DC algorithms / Image deblurring / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 405, IBISML2022-86, pp. 111-118, March 2023. | |
| Paper # | IBISML2022-86 | |
| Date of Issue | 2023-02-23 (PRMU, IBISML) | |
| 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 | PRMU2022-79 IBISML2022-86 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2023-03-02 - 2023-03-03 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Future University Hakodate |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2023-03-PRMU-IBISML-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Blind deconvolution with non-smooth regularization via Bregman proximal DC algorithms |
| Sub Title (in English) | |
| Keyword(1) | Blind deconvolution |
| Keyword(2) | DC optimization |
| Keyword(3) | Bregman proximal DC algorithms |
| Keyword(4) | Image deblurring |
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| Keyword(8) | |
| 1st Author's Name | Shota Takahashi |
| 1st Author's Affiliation | The Graduate University for Advanced Studies (SOKENDAI) |
| 2nd Author's Name | Mirai Tanaka |
| 2nd Author's Affiliation | The Institute of Statistical Mathematics (ISM) |
| 3rd Author's Name | Ikeda Shiro |
| 3rd Author's Affiliation | The Institute of Statistical Mathematics (ISM) |
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| Speaker | Author-1 |
| Date Time | 2023-03-02 10:25:00 |
| Presentation Time | 15 minutes |
| Registration for | IBISML |
| Paper # | PRMU2022-79, IBISML2022-86 |
| Volume (vol) | vol.122 |
| Number (no) | no.404(PRMU), no.405(IBISML) |
| Page | pp.111-118 |
| #Pages | 8 |
| Date of Issue | 2023-02-23 (PRMU, IBISML) |
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