Self-supervised Low-Light Image Enhancement via Histogram Equalization Prior

Author:

Zhang Feng,Shao Yuanjie,Sun Yishi,Gao Changxin,Sang Nong

Publisher

Springer Nature Singapore

Reference25 articles.

1. Chan, S.H., Khoshabeh, R., Gibson, K.B., Gill, P.E., Nguyen, T.Q.: An augmented lagrangian method for total variation video restoration. IEEE Trans. Image Process. 20(11), 3097–3111 (2011)

2. Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: British Machine Vision Conference. British Machine Vision Association (2018)

3. Dong, X., et al.: Fast efficient algorithm for enhancement of low lighting video. In: 2011 IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE (2011)

4. Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv:1811.12231 (2018)

5. Guo, C., et al.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1780–1789 (2020)

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