Benchmarking Low-Light Image Enhancement and Beyond

By Jiaying Liu, Dejia Xu, Wenhan Yang, Minhao Fan, Haofeng Huang. International Journal of Computer Vision, 2021

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Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, methodology): “…et al. 2018), DSLR (Lim and Kim 2020), KinD (Zhang, Zhang, and Guo 2019)), two unsupervised CNN-based methods (EL-GAN (Jiang et al. 2021), RUAS (Liu et al. 2021b)), two zero-shot learning-based methods (Z_DCE (Guo et al. 2020), Z_DCE++ (Li, Guo, and Loy 2021) ) and two supervised…”
URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement (2022, background): “Images captured in a poor light environment always suffer from low contrast and low visibility, which pose challenges for both human visualization and numerous high-level vision tasks such as object detection [21, 23, 35].”
Low-Light Image and Video Enhancement Using Deep Learning: A Survey (2021, methodology): “In addition to the testing subsets in the paired datasets [2], [4], [6], [8], [9], [55], [79], there are several testing data collected from related works or commonly used for experimental comparisons.”
Low-Light Image Enhancement with Normalizing Flow (2021, methodology): “For VE-LOL dataset, we train the model for 4 × 10 4 iterations and the learning rate is decreased with a factor of 0 .”
Generative Diffusion Prior for Unified Image Restoration and Enhancement (2023, methodology): “Qualitative results of low-light enhancement on the LOL [83], VE-LOL [45], and LoLi-Phone [39] datasets.”