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- Publisher Website: 10.1109/ICPR48806.2021.9411979
- Scopus: eid_2-s2.0-85110422752
- WOS: WOS:000678409207060
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Conference Paper: MBD-GAN: Model-based image deblurring with a generative adversarial network
Title | MBD-GAN: Model-based image deblurring with a generative adversarial network |
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Authors | |
Keywords | deep learning (artificial intelligence) image reconstruction image resolution neural nets |
Issue Date | 2021 |
Publisher | IEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545 |
Citation | Proceedings of 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10-15 January 2021, p. 7306-7313 How to Cite? |
Abstract | This paper presents a methodology to tackle inverse imaging problems by leveraging the synergistic power of imaging model and deep learning. The premise is that while learning-based techniques have quickly become the methods of choice in various applications, they often ignore the prior knowledge embedded in imaging models. Incorporating the latter has the potential to improve the image estimation. Specifically, we first provide a mathematical basis of using generative adversarial network (GAN) in inverse imaging through considering an optimization framework. Then, we develop the specific architecture that connects the generator and discriminator networks with the imaging model. While this technique can be applied to a variety of problems, from image reconstruction to super-resolution, we take image deblurring as the example here, where we show in detail the implementation and experimental results of what we call the model-based deblurring GAN (MBD-GAN). |
Persistent Identifier | http://hdl.handle.net/10722/304346 |
ISSN | 2020 SCImago Journal Rankings: 0.276 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Song, L | - |
dc.contributor.author | Lam, EYM | - |
dc.date.accessioned | 2021-09-23T08:58:46Z | - |
dc.date.available | 2021-09-23T08:58:46Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Proceedings of 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10-15 January 2021, p. 7306-7313 | - |
dc.identifier.issn | 1051-4651 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304346 | - |
dc.description.abstract | This paper presents a methodology to tackle inverse imaging problems by leveraging the synergistic power of imaging model and deep learning. The premise is that while learning-based techniques have quickly become the methods of choice in various applications, they often ignore the prior knowledge embedded in imaging models. Incorporating the latter has the potential to improve the image estimation. Specifically, we first provide a mathematical basis of using generative adversarial network (GAN) in inverse imaging through considering an optimization framework. Then, we develop the specific architecture that connects the generator and discriminator networks with the imaging model. While this technique can be applied to a variety of problems, from image reconstruction to super-resolution, we take image deblurring as the example here, where we show in detail the implementation and experimental results of what we call the model-based deblurring GAN (MBD-GAN). | - |
dc.language | eng | - |
dc.publisher | IEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000545 | - |
dc.relation.ispartof | International Conference on Pattern Recognition | - |
dc.rights | International Conference on Pattern Recognition. Copyright © IEEE, Computer Society. | - |
dc.rights | ©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | deep learning (artificial intelligence) | - |
dc.subject | image reconstruction | - |
dc.subject | image resolution | - |
dc.subject | neural nets | - |
dc.title | MBD-GAN: Model-based image deblurring with a generative adversarial network | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lam, EYM: elam@eee.hku.hk | - |
dc.identifier.authority | Lam, EYM=rp00131 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/ICPR48806.2021.9411979 | - |
dc.identifier.scopus | eid_2-s2.0-85110422752 | - |
dc.identifier.hkuros | 325010 | - |
dc.identifier.spage | 7306 | - |
dc.identifier.epage | 7313 | - |
dc.identifier.isi | WOS:000678409207060 | - |
dc.publisher.place | United States | - |