弱光图像增强(Low-light image enhancement)资料整理（更新中...）

  前一段整理了近几年的弱光图像的相关论文和代码，故在这里做一个汇总，以方便大家和有需要的同学们查阅。(这里整理的主要是基于深度学习的方法)

常用数据集：

一、Paired
1、LOL(500张)：https://daooshee.github.io/BMVC2018website/
Cite from: Wei C, Wang W, Yang W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv:1808.04560, 2018.

2、SCIE(4413张)：https://github.com/csjcai/SICE
Cite from： Cai J, Gu S, Zhang L. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049-2062.

3、SID(5094张)： https://github.com/cchen156/Learning-to-See-in-the-Dark
Cite from： Chen C, Chen Q, Xu J, et al. Learning to see in the dark[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3291-3300.

二、Unpaired

1、LIME(10张)： https://drive.google.com/file/d/0BwVzAzXoqrSXb3prWUV1YzBjZzg/view
Cite from： Guo X, Li Y, Ling H. LIME: Low-light image enhancement via illumination map estimation[J]. IEEE Transactions on image processing, 2016, 26(2): 982-993.

2、NPE(84张)： https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
Cite from： Wang S, Zheng J, Hu H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images[J]. IEEE transactions on image processing, 2013, 22(9): 3538-3548.

3、MEF(17张)： https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
Cite from： Ma K, Zeng K, Wang Z. Perceptual quality assessment for multi-exposure image fusion[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3345-3356.

4、DICM(64张)： https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
Cite from： C. Lee, C. Lee and C. -S. Kim, “Contrast enhancement based on layered difference representation,” 2012 19th IEEE International Conference on Image Processing, 2012, pp. 965-968, doi: 10.1109/ICIP.2012.6467022.

5、VV(24张)： https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
Cite from： Vonikakis V, Andreadis I, Gasteratos A. Fast centre–surround contrast modification[J]. IET Image processing, 2008, 2(1): 19-34.

 
论文汇总：

【2022年】
1、（2022_CVPR）Toward Fast, Flexible, and Robust Low-Light Image Enhancement
Cite from： Ma L, Ma T, Liu R, et al. Toward Fast, Flexible, and Robust Low-Light Image Enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 5637-5646.
【Paper】【Code_PyTorch】

2、（2022_CVPR）URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement
Cite from： Wu W, Weng J, Zhang P, et al. URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 5901-5910.
【Paper】【Code_Pytorch】

3、（2022_CVPR）SNR-aware Low-Light Image Enhancement
Cite from： Xu X, Wang R, Fu C W, et al. SNR-Aware Low-Light Image Enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 17714-17724.
【Paper】【Code_Pytorch】

 

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【2020年】

1、（2020_CVPR）Learning to restore low-light images via decomposition-and-enhancemen
Cite from： Xu K, Yang X, Yin B, et al. Learning to restore low-light images via decomposition-and-enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 2281-2290.
【Paper】【Code】

2、（2020_CVPR）Zero-reference deep curve estimation for low-light image enhancement
Cite from： Guo C, Li C, Guo J, et al. Zero-reference deep curve estimation for low-light image enhancement[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1780-1789.
【Paper】【Code_Pytorch】

3、（2020_CVPR）From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement
Cite from： Yang W, Wang S, Fang Y, et al. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 3063-3072.
【Paper】【Code】

4、（2020_ACMMM）Integrating semantic segmentation and retinex model for low light image enhancement
Cite from： Fan M, Wang W, Yang W, et al. Integrating semantic segmentation and retinex model for low-light image enhancement[C]//Proceedings of the 28th ACM international conference on multimedia. 2020: 2317-2325.
【Paper 】【Code】

5、（2020_AAAI）EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network
Cite from： Zhu M, Pan P, Chen W, et al. Eemefn: Low-light image enhancement via edge-enhanced multi-exposure fusion network[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(07): 13106-13113.
【Paper】【Code】

 

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【2019年】

1、（2019_ICCV）Seeing motion in the dark
Cite from： Chen C, Chen Q, Do M N, et al. Seeing motion in the dark[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 3185-3194.
【Paper】【Code_TensorFlow】

2、（2019_CVPR）Underexposed Photo Enhancement Using Deep Illumination Estimation
Cite from： Wang R, Zhang Q, Fu C W, et al. Underexposed photo enhancement using deep illumination estimation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 6849-6857.
【Paper】【Code_TensorFlow】

3、（2019_ACMMM）Zero-shot restoration of back-lit images using deep internal
learning
Cite from： Zhang L, Zhang L, Liu X, et al. Zero-shot restoration of back-lit images using deep internal learning[C]//Proceedings of the 27th ACM International Conference on Multimedia. 2019: 1623-1631.
【Paper 】【Code_TensorFlow】

4、（2019_ACMMM）Kindling the Darkness: A Practical Low-light Image Enhancer
Cite from： Zhang Y, Zhang J, Guo X. Kindling the darkness: A practical low-light image enhancer[C]//Proceedings of the 27th ACM international conference on multimedia. 2019: 1632-1640.
【Paper 】【Code_TensorFlow】

5、（2019_TIP）EnlightenGAN: Deep light enhancement without paired supervision
Cite from： Jiang Y, Gong X, Liu D, et al. Enlightengan: Deep light enhancement without paired supervision[J]. IEEE Transactions on Image Processing, 2021, 30: 2340-2349.
【Paper】【Code】

6、（2019_TIP）Low-light image enhancement via a deep hybrid network
Cite from： Ren W, Liu S, Ma L, et al. Low-light image enhancement via a deep hybrid network[J]. IEEE Transactions on Image Processing, 2019, 28(9): 4364-4375.
【Paper】【Code】

7、（2019_ACMMM）Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement
Cite from： Wang Y, Cao Y, Zha Z J, et al. Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement[C]//Proceedings of the 27th ACM international conference on multimedia. 2019: 2015-2023.
【Paper 】【Code_Pytorch】

 

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【2018年】

1、（2018_BMVC）MBLLEN: Low-light Image/Video Enhancement Using CNNs
Cite from： Lv F, Lu F, Wu J, et al. MBLLEN: Low-Light Image/Video Enhancement Using CNNs[C]//BMVC. 2018, 220(1): 4.
【Paper】【Code_TensorFlow】

2、（2018_PRL）LightenNet: A convolutional neural network for weakly illuminated image enhancement
Cite from： Li C, Guo J, Porikli F, et al. LightenNet: A convolutional neural network for weakly illuminated image enhancement[J]. Pattern recognition letters, 2018, 104: 15-22.
【Paper】【Code_Caffe&Matlab】

3、（2018_BMVC）Deep retinex decomposition for low-light enhancement
Cite from： Wei C, Wang W, Yang W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv:1808.04560, 2018.
【Paper】【Code_TensorFlow】

4、（2018_TIP）Learning a deep single image contrast enhancer from multi-exposure images
Cite from： Cai J, Gu S, Zhang L. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049-2062.
【Paper】【Code_Caffe&Matlab】

5、（2018_CVPR）Learning to see in the dark
Cite from： Chen C, Chen Q, Xu J, et al. Learning to see in the dark[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3291-3300.
【Paper】【Code_TensorFlow】

6、（2018_NeurIPS）DeepExposure: Learning to expose photos with asynchronously reinforced adversarial learning
Cite from： Yu R, Liu W, Zhang Y, et al. Deepexposure: Learning to expose photos with asynchronously reinforced adversarial learning[J]. Advances in Neural Information Processing Systems, 2018, 31.
【Paper】【Code_暂无】

 

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【2017年】

1、（2017_TOG）Deep Bilateral Learning for Real-Time Image Enhancement (HDR-Net)
Cite from： Gharbi M, Chen J, Barron J T, et al. Deep bilateral learning for real-time image enhancement[J]. ACM Transactions on Graphics (TOG), 2017, 36(4): 1-12.
【Paper】【Code】

2、（2017_PR）LLNet: A deep autoencoder approach to natural low-light image enhancement
Cite from： Lore K G, Akintayo A, Sarkar S. LLNet: A deep autoencoder approach to natural low-light image enhancement[J]. Pattern Recognition, 2017, 61: 650-662.
【Paper】【Code_Theano】