Low-light Enhancement暗光图像增强论文整理2019-2021

github代码都放后面了，没有就是找不到代码

近年论文

  • Guo, Xiaojie et al. “LIME: Low-Light Image Enhancement via Illumination Map Estimation.” IEEE Transactions on Image Processing 26 (2017): 982-993.https://github.com/estija/LIME
  • Fan, Minhao et al. “Integrating Semantic Segmentation and Retinex Model for Low-Light Image Enhancement.” Proceedings of the 28th ACM International Conference on Multimedia (2020)
  • Xu, Ke et al. “Learning to Restore Low-Light Images via Decomposition-and-Enhancement.” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020): 2278-2287.
  • Yang, W. et al. “Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement.” IEEE Transactions on Image Processing 30 (2021): 2072-2086.
  • Ren, Xutong et al. “LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model.” IEEE Transactions on Image Processing 29 (2020): 5862-5876.
  • Jiang, Yifan et al. “EnlightenGAN: Deep Light Enhancement Without Paired Supervision.” IEEE Transactions on Image Processing 30 (2021): 2340-2349.
  • Ren, W. et al. “Low-Light Image Enhancement via a Deep Hybrid Network.” IEEE Transactions on Image Processing 28 (2019): 4364-4375. .https://drive.google.com/file/d/1WYQd5z9NXW-IOWLSH3w70t3XnLUAHnAZ/view
  • Li, Mading et al. “Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model.” IEEE Transactions on Image Processing 27 (2018): 2828-2841.
  • Fu, Gang et al. “A Hybrid L2 −LP Variational Model For Single Low-Light Image Enhancement With Bright Channel Prior.” 2019 IEEE International Conference on Image Processing (ICIP) (2019): 1925-1929.
  • Loh, Y. et al. “Low-light image enhancement using Gaussian Process for features retrieval.” Signal Process. Image Commun. 74 (2019): 175-190. https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/tree/master/SPIC
  • Li, Chongyi et al. “NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal.” Proceedings of the 28th ACM International Conference on Multimedia (2020)
  • Zhang, Yonghua et al. “Kindling the Darkness: A Practical Low-light Image Enhancer.” Proceedings of the 27th ACM International Conference on Multimedia (2019)
  • Lamba, Mohit et al. “Towards Fast and Light-Weight Restoration of Dark Images.” ArXiv abs/2011.14133 (2020)[code]
  • Chen, Chen et al. “Learning to See in the Dark.” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): 3291-3300.[code][code-pytorch]
  • Wei, Chen et al. “Deep Retinex Decomposition for Low-Light Enhancement.” BMVC (2018).[code][code-pytorch]
  • Gharbi, Michaël et al. “Deep bilateral learning for real-time image enhancement.” ACM Transactions on Graphics (TOG) 36 (2017): 1 - 12.[code]
  • Wang, R. et al. “Underexposed Photo Enhancement Using Deep Illumination Estimation.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019): 6842-6850.[code (only test)]
  • Parihar, A. et al. “A Comparative Analysis of Deep Learning based Approaches for Low-Light Image Enhancement.” 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (2020): 1-5.[code]
  • Zhang, Yonghua et al. “Kindling the Darkness: A Practical Low-light Image Enhancer.” Proceedings of the 27th ACM International Conference on Multimedia (2019). [code]
  • Wei, Kaixuan et al. “A Physics-Based Noise Formation Model for Extreme Low-Light Raw Denoising.” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020): 2755-2764.[ELD dataset] [paper][Code]
  • Wang, Junyi et al. “RDGAN: Retinex Decomposition Based Adversarial Learning for Low-Light Enhancement.” 2019 IEEE International Conference on Multimedia and Expo (ICME) (2019): 1186-1191.[code]

半监督无监督（WSL）、无需成对数据（Unpaired）

  • Yang, W. et al. “From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement.” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020): 3060-3069.（Unpaired or WSL）
  • Li, Chongyi et al. “Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation.” IEEE transactions on pattern analysis and machine intelligence PP (2021)（Unpaired or WSL）
  • Lee, Hunsang et al. “Unsupervised Low-Light Image Enhancement Using Bright Channel Prior.” IEEE Signal Processing Letters 27 (2020): 251-255.（Unpaired or WSL）
  • Xiong, Wei et al. “Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks.” ArXiv abs/2005.02818 (2020)（Unpaired or WSL）
  • Qu, Yangyang et al. “LEUGAN: Low-Light Image Enhancement by Unsupervised Generative Attentional Networks.” ArXiv abs/2012.13322 (2020)（Unpaired or WSL）
  • Zhang, Y. et al. “Self-supervised Image Enhancement Network: Training with Low Light Images Only.” ArXiv abs/2002.11300 (2020)（Unpaired or WSL）https://github.com/hitzhangyu/Self-supervised-Image-Enhancement-Network-Training-With-Low-Light-Images-Only
  • Li, Chongyi et al. “Flexible Piecewise Curves Estimation for Photo Enhancement.” ArXiv abs/2010.13412 (2020)（Unpaired or WSL）
  • Jiang, Yifan et al. “EnlightenGAN: Deep Light Enhancement Without Paired Supervision.” IEEE Transactions on Image Processing 30 (2021): 2340-2349.（Unpaired or WSL）https://github.com/VITA-Group/EnlightenGAN
  • Guo, C. et al. “Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement.” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020): 1777-1786.https://github.com/Li-Chongyi/Zero-DCE

数据榜单

https://paperswithcode.com/task/low-light-image-enhancement

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