Beyond Brightening Low-light Images

By Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, Jiawan Zhang. International Journal of Computer Vision, 2021

995 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5e87044d805a43823ace73cb8f2bf1f4058ccf47

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ming-Hsuan Yang
2. Luc Van Gool
3. Fahad Shahbaz Khan
4. Ling Shao
5. Radu Timofte
6. Salman Hameed Khan
7. Chen Change Loy
8. Dacheng Tao

In Media Technology

1. Radu Timofte
2. Yulun Zhang
3. Ming-Hsuan Yang
4. Salman Hameed Khan
5. Fahad Shahbaz Khan
6. Munawar Hayat
7. Syed Waqas Zamir
8. Luc Van Gool

Citations in the corpus

• Low-Light Image Enhancement with Normalizing Flow (2021, background): “(Ying, Li, and Gao 2017) 15.95 0.6386 0.4573 DeepUPE (Wang et al. 2019a) 13.19 0.4902 0.4634 JED (Ren et al. 2018) 16.73 0.6817 0.3899 LIME (Guo, Li, and Ling 2016) 14.07 0.5274 0.4021 SICE (Cai, Gu, and Zhang 2018) 18.06 0.7094 0.5078 LLNet (Lore et al. 2017) 17.57 0.7388 0.4021 SRIE (Fu et al. 201”
• Learning Enriched Features for Fast Image Restoration and Enhancement (2022, methodology): “Method BIMEF CRM Dong LIME MF RRM SRIE Retinex-Net MSR NPE GLAD KinD KinD++ MIRNet-v2 [126] [127] [128] [129] [130] [131] [130] [85] [81] [132] [133] [4] [134] (Ours)”
• Toward Fast, Flexible, and Robust Low-Light Image Enhancement (2022, neutral): “The smoothness property of the illumination is a broad consensus in this task [7,34].”
• URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement (2022, methodology): “Furthermore, to verify the efficiency of our model in learning implicit prior from data, we compare with state-of-the-art (SOTA) learning-based methods including Retinex-Net [34], KinD [41], Zero-DCE [11], RUAS [24], AGLL-Net [26] and KinD++ [40].”
• Generative Diffusion Prior for Unified Image Restoration and Enhancement (2023, background): “17 [102] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang.”
• Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement (2023, background): “These methods [54, 65, 66] usually suffer from a multi-stage training pipeline.”