EnlightenGAN: Deep Light Enhancement Without Paired Supervision

@article{Jiang2021EnlightenGANDL,
  title={EnlightenGAN: Deep Light Enhancement Without Paired Supervision},
  author={Yi-fan Jiang and Xinyu Gong and Ding Liu and Yu Cheng and Chen Fang 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}
}
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. [] Key Method 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…

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