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Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Fang Chen, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
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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Jiang, Y., Gong, X., Liu, D., Cheng, Y., Chen, F., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021). EnlightenGAN: Deep Light Enhancement Without Paired Supervision. IEEE Transactions on Image Processing, 30, 2340–2349. https://doi.org/10.1109/tip.2021.3051462
Jiang, Yifan, et al. "EnlightenGAN: Deep Light Enhancement Without Paired Supervision." IEEE Transactions on Image Processing, vol. 30, 2021, pp. 2340–2349, https://doi.org/10.1109/tip.2021.3051462.
Jiang, Yifan, Xinyu Gong, Ding Liu, Yu Cheng, Fang Chen, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. "EnlightenGAN: Deep Light Enhancement Without Paired Supervision." IEEE Transactions on Image Processing 30 (2021): 2340–2349. https://doi.org/10.1109/tip.2021.3051462.
Jiang Y., Gong X., Liu D. [и др.] EnlightenGAN: Deep Light Enhancement Without Paired Supervision // IEEE Transactions on Image Processing. — 2021. — Vol. 30. — P. 2340–2349. — URL: https://doi.org/10.1109/tip.2021.3051462
@article{jiang2021enlightengan,
title = {{EnlightenGAN: Deep Light Enhancement Without Paired Supervision}},
author = {Yifan Jiang and Xinyu Gong and Ding Liu and Yu Cheng and Fang Chen and Xiaohui Shen and Jianchao Yang and Pan Zhou and Zhangyang Wang},
journal = {IEEE Transactions on Image Processing},
year = {2021},
volume = {30},
pages = {2340--2349},
doi = {10.1109/tip.2021.3051462},
url = {https://doi.org/10.1109/tip.2021.3051462}
}TY - JOUR AU - Jiang, Yifan AU - Gong, Xinyu AU - Liu, Ding AU - Cheng, Yu AU - Chen, Fang AU - Shen, Xiaohui AU - Yang, Jianchao AU - Zhou, Pan AU - Wang, Zhangyang TI - EnlightenGAN: Deep Light Enhancement Without Paired Supervision JO - IEEE Transactions on Image Processing VL - 30 SP - 2340 EP - 2349 PY - 2021 DA - 2021/01/01 DO - 10.1109/tip.2021.3051462 UR - https://doi.org/10.1109/tip.2021.3051462 ER -
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