EnlightenGAN: Deep Light Enhancement Without Paired Supervision
Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challeng...
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| Vydané v: | IEEE transactions on image processing Ročník 30; s. 2340 - 2349 |
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| Hlavní autori: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
| On-line prístup: | Získať plný text |
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| Abstract | Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. We propose a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN , that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself, and benchmark a series of innovations for the low-light image enhancement problem, including a global-local discriminator structure, a self-regularized perceptual loss fusion, and the attention mechanism. Through extensive experiments, our proposed approach outperforms recent methods under a variety of metrics in terms of visual quality and subjective user study. Thanks to the great flexibility brought by unpaired training, EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world images from various domains. Our codes and pre-trained models are available at: https://github.com/VITA-Group/EnlightenGAN . |
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| AbstractList | Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. We propose a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself, and benchmark a series of innovations for the low-light image enhancement problem, including a global-local discriminator structure, a self-regularized perceptual loss fusion, and the attention mechanism. Through extensive experiments, our proposed approach outperforms recent methods under a variety of metrics in terms of visual quality and subjective user study. Thanks to the great flexibility brought by unpaired training, EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world images from various domains. Our codes and pre-trained models are available at: https://github.com/VITA-Group/EnlightenGAN. Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. We propose a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself, and benchmark a series of innovations for the low-light image enhancement problem, including a global-local discriminator structure, a self-regularized perceptual loss fusion, and the attention mechanism. Through extensive experiments, our proposed approach outperforms recent methods under a variety of metrics in terms of visual quality and subjective user study. Thanks to the great flexibility brought by unpaired training, EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world images from various domains. Our codes and pre-trained models are available at: https://github.com/VITA-Group/EnlightenGAN.Deep learning-based methods have achieved remarkable success in image restoration and enhancement, but are they still competitive when there is a lack of paired training data? As one such example, this paper explores the low-light image enhancement problem, where in practice it is extremely challenging to simultaneously take a low-light and a normal-light photo of the same visual scene. We propose a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images. Instead of supervising the learning using ground truth data, we propose to regularize the unpaired training using the information extracted from the input itself, and benchmark a series of innovations for the low-light image enhancement problem, including a global-local discriminator structure, a self-regularized perceptual loss fusion, and the attention mechanism. Through extensive experiments, our proposed approach outperforms recent methods under a variety of metrics in terms of visual quality and subjective user study. Thanks to the great flexibility brought by unpaired training, EnlightenGAN is demonstrated to be easily adaptable to enhancing real-world images from various domains. Our codes and pre-trained models are available at: https://github.com/VITA-Group/EnlightenGAN. |
| Author | Jiang, Yifan Liu, Ding Cheng, Yu Fang, Chen Gong, Xinyu Yang, Jianchao Zhou, Pan Wang, Zhangyang Shen, Xiaohui |
| Author_xml | – sequence: 1 givenname: Yifan orcidid: 0000-0002-7113-305X surname: Jiang fullname: Jiang, Yifan email: yifanjiang97@utexas.edu organization: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA – sequence: 2 givenname: Xinyu orcidid: 0000-0002-6993-136X surname: Gong fullname: Gong, Xinyu email: xinyu.gong@utexas.edu organization: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA – sequence: 3 givenname: Ding orcidid: 0000-0002-0931-1345 surname: Liu fullname: Liu, Ding email: liuding@bytedance.com organization: Bytedance Inc., Mountain View, CA, USA – sequence: 4 givenname: Yu surname: Cheng fullname: Cheng, Yu email: yu.cheng@microsoft.com organization: Microsoft AI and Research, Redmond, WA, USA – sequence: 5 givenname: Chen surname: Fang fullname: Fang, Chen email: fangchen@bytedance.com organization: Bytedance Inc., Mountain View, CA, USA – sequence: 6 givenname: Xiaohui surname: Shen fullname: Shen, Xiaohui email: shenxiaohui.kevin@bytedance.com organization: Bytedance Inc., Mountain View, CA, USA – sequence: 7 givenname: Jianchao surname: Yang fullname: Yang, Jianchao email: yangjianchao@bytedance.com organization: Bytedance Inc., Mountain View, CA, USA – sequence: 8 givenname: Pan orcidid: 0000-0002-8629-4622 surname: Zhou fullname: Zhou, Pan email: panzhou@hust.edu.cn organization: Department of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan, China – sequence: 9 givenname: Zhangyang orcidid: 0000-0002-2050-5693 surname: Wang fullname: Wang, Zhangyang email: atlaswang@utexas.edu organization: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33481709$$D View this record in MEDLINE/PubMed |
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| CODEN | IIPRE4 |
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| Title | EnlightenGAN: Deep Light Enhancement Without Paired Supervision |
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