[2] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. “LLNet: A deep autoencoder approach to natural low-light image enhancement”. In: Pattern Recognition 61 (2017), pp. 650–662.
[3] Yifan Jiang et al. EnlightenGAN: Deep Light Enhancement without Paired Supervision. 2019. DOI: 10 . 48550 / ARXIV.1906.06972. URL: https://arxiv.org/abs/1906.06972.
[4] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015. DOI: 10.48550/ARXIV.1505.04597. URL: https://arxiv.org/abs/1505.04597.
[5] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the Darkness: A Practical Low-light Image Enhancer. 2019. DOI: 10.48550/ARXIV.1905.04161. URL: https://arxiv.org/abs/1905.04161.
[6] Chen Wei et al. “Deep Retinex Decomposition for Low-Light Enhancement”. In: British Machine Vision Conference. 2018.
[7] Vladimir Bychkovsky et al. “Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs”. In: The Twenty-Fourth IEEE Conference on Computer Vision and Pattern Recognition. 2011.