低光图像增强（Low-light image enhancement）文章整理_histogram-based transformation function estimation

低光图像增强（Low-light image enhancement）文章整理

低光图像增强是图像增强任务中的重要组成部分，目前对于低光图像增强方法的整理参差不全。因此希望在以有的文章基础上整理汇总一下现有的低光图像增强算法（文章和代码）。希望为自己以及大家查找低光图像增强领域的文章和代码提供一些便捷。

常用网址

首先介绍一些比较适用的网址，前三个是github上对低光图像增强进行整理的网址，第四个是paperswithcode网站上关于低光图像整理的链接。

1. https://github.com/Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open
2. https://github.com/baidut/OpenCE
3. https://github.com/dawnlh/low-light-image-enhancement-resources
4. https://paperswithcode.com/task/low-light-image-enhancement

常用数据集

1. LOL: https://daooshee.github.io/BMVC2018website/
   Cite as: Wei C, Wang W, Yang W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv:1808.04560, 2018.

2. MEF: https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
   Cite as: 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.

3. SID: https://github.com/cchen156/Learning-to-See-in-the-Dark
   Cite as: 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.

4. VV: https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T

5. DICM: https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
   Cite as: Lee C, Lee C, Kim C S. Contrast enhancement based on layered difference representation[C]//2012 19th IEEE International Conference on Image Processing. IEEE, 2012: 965-968.

6. LIME: https://drive.google.com/file/d/0BwVzAzXoqrSXb3prWUV1YzBjZzg/view
   Cite as: 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.

7. SCIE: https://github.com/csjcai/SICE
   Cite as: 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.

8. NPE: https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
   Cite as: 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.

论文列表

【2022】

1、文章：DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations【无监督学习】【用户自定义增强】

Cite as: Tang, Linfeng, et al. “DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations.” IEEE Transactions on Neural Networks and Learning Systems (2022). .

Paper: https://ieeexplore.ieee.org/abstract/document/9833451/

Code: https://github.com/Linfeng-Tang/DRLIE【TensorFlow】

【2021】

1、文章：EnlightenGAN: Deep Light Enhancement Without Paired Supervision【半监督学习】【GAN】

Cite as: Y. Jiang et al., “EnlightenGAN: Deep Light Enhancement Without Paired Supervision,” in IEEE Transactions on Image Processing, vol. 30, pp. 2340-2349, 2021, doi: 10.1109/TIP.2021.3051462 .

Paper: https://ieeexplore.ieee.org/abstract/document/9334429

Code: https://github.com/VITA-Group/EnlightenGAN 【Pytorch】


2、文章：Beyond Brightening Low-light Images【监督学习】【Retinex】

Cite as: Zhang Y, Guo X, Ma J, et al. Beyond Brightening Low-light Images[J]. International Journal of Computer Vision, 2021, 129(4): 1013-1037.

Paper: https://link.springer.com/article/10.1007/s11263-020-01407-x

Code: https://github.com/zhangyhuaee/KinD 【Tensorflow】

【2020】

1、文章： Zero-reference deep curve estimation for low-light image enhancement【Zero-short Learning】

Cite as: 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: https://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.html

Code: https://github.com/Li-Chongyi/Zero-DCE 【Pytorch】


2、文章：From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement【半监督学习】

Cite as: 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: https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_From_Fidelity_to_Perceptual_Quality_A_Semi-Supervised_Approach_for_Low-Light_CVPR_2020_paper.html

Code: https://github.com/flyywh/CVPR-2020-Semi-Low-Light 【Pytorch】


3、文章：From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement【半监督学习】

Cite as: 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: https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_From_Fidelity_to_Perceptual_Quality_A_Semi-Supervised_Approach_for_Low-Light_CVPR_2020_paper.html

Code: https://github.com/flyywh/CVPR-2020-Semi-Low-Light 【Pytorch】

4、文章：Fast enhancement for non-uniform illumination images using light-weight CNNs【监督学习】

Cite as: Lv F, Liu B, Lu F. Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs[C]//Proceedings of the 28th ACM International Conference on Multimedia. 2020: 1450-1458.

Paper: https://dl.acm.org/doi/abs/10.1145/3394171.3413925

Code: 未开源【TensorFlow】


5、文章：Integrating semantic segmentation and retinex model for low light image enhancement【Retinex 】

Cite as: 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: https://dl.acm.org/doi/abs/10.1145/3394171.3413757

Code: 未开源


6、文章：Learning to restore low-light images via decomposition-and-enhancement

Cite as: 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: https://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Learning_to_Restore_Low-Light_Images_via_Decomposition-and-Enhancement_CVPR_2020_paper.html

Code: 未开源【PyTorch】

7、文章：EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network【Multi-exposure Fusion】

Cite as: 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: https://ojs.aaai.org/index.php/AAAI/article/view/7013

Code: 未开源【Pytorch】


8、文章：Lightening network for low-light image enhancement

Cite as: Wang L W, Liu Z S, Siu W C, et al. Lightening network for low-light image enhancement[J]. IEEE Transactions on Image Processing, 2020, 29: 7984-7996.

Paper: https://ieeexplore.ieee.org/abstract/document/9141197

Code: 未开源【Pytorch】


9、文章：Luminance-aware pyramid network for low-light image enhancement

Cite as: Li J, Li J, Fang F, et al. Luminance-aware Pyramid Network for Low-light Image Enhancement[J]. IEEE Transactions on Multimedia, 2020.

Paper: https://ieeexplore.ieee.org/abstract/document/9186194

Code: 未开源【Pytorch】


10、文章： Low light video enhancement using synthetic data produced with an intermediate domain mapping

Cite as: Triantafyllidou D, Moran S, McDonagh S, et al. Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping[C]//European Conference on Computer Vision. Springer, Cham, 2020: 103-119.

Paper: https://link.springer.com/chapter/10.1007/978-3-030-58601-0_7

Code: 未开源【Tensorflow】


11、文章：TBEFN: A two-branch exposure-fusion network for low-light image enhancement

Cite as: Lu K, Zhang L. TBEFN: A two-branch exposure-fusion network for low-light image enhancement[J]. IEEE Transactions on Multimedia, 2020.

Paper: https://ieeexplore.ieee.org/abstract/document/9261119/

Code: https://github.com/lukun199/TBEFN【Tensorflow】


12、文章：Zero-shot restoration of underexposed images via robust retinex decomposition 【Zero-short Learning】【Retinex】

Cite as: Zhu A, Zhang L, Shen Y, et al. Zero-shot restoration of underexposed images via robust retinex decomposition[C]//2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020: 1-6.

Paper: https://ieeexplore.ieee.org/abstract/document/9102962/

Code: https://aaaaangel.github.io/RRDNet-Homepage【Pytorch】


**13、文章：DSLR: Deep stacked laplacian restorer for low-light image enhancement **

Cite as: Lim S, Kim W. DSLR: Deep Stacked Laplacian Restorer for Low-light Image Enhancement[J]. IEEE Transactions on Multimedia, 2020.

Paper: https://ieeexplore.ieee.org/abstract/document/9264763/

Code: https://github.com/SeokjaeLIM/DSLR-release【Pytorch】

【2019】

1、文章：Seeing motion in the dark

Cite as: 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: https://openaccess.thecvf.com/content_ICCV_2019/html/Chen_Seeing_Motion_in_the_Dark_ICCV_2019_paper.html

Code: https://github.com/cchen156/Seeing-Motion-in-the-Dark【TensorFlow】


2、文章：Learning to see moving object in the dark

Cite as: Jiang H, Zheng Y. Learning to see moving objects in the dark[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019: 7324-7333.

Paper: https://openaccess.thecvf.com/content_ICCV_2019/html/Jiang_Learning_to_See_Moving_Objects_in_the_Dark_ICCV_2019_paper.html

Code: https://github.com/MichaelHYJiang【TensorFlow】


3、文章：Underexposed photo enhancement using deep illumination estimation

Cite as: 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: https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Underexposed_Photo_Enhancement_Using_Deep_Illumination_Estimation_CVPR_2019_paper.html

Code: https://github.com/Jia-Research-Lab/DeepUPE【TensorFlow】


4、文章：Kindling the darkness: A practical low-light image enhancer 【Retinex】

Cite as: 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: https://dl.acm.org/doi/abs/10.1145/3343031.3350926

Code: https://github.com/zhangyhuaee/KinD【TensorFlow】

5、文章：Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement【Retinex】

Cite as: 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: https://dl.acm.org/doi/abs/10.1145/3343031.3350983

Code: 未开源【Caffe】

6、文章：Low-light image enhancement via a deep hybrid network

Cite as: 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: https://ieeexplore.ieee.org/abstract/document/8692732/

Code: 未开源【Caffe】


7、文章：Zero-shot restoration of back-lit images using deep internal learning

Cite as: 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: https://dl.acm.org/doi/abs/10.1145/3343031.3351069

Code: https://cslinzhang.github.io/ExCNet/【PyTorch】

【2018】

1、文章：LightenNet: A convolutional neural network for weakly illuminated image enhancement

Cite as: 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: https://www.sciencedirect.com/science/article/abs/pii/S0167865518300163

Code: https://li-chongyi.github.io/proj_lowlight.html【Caffe & MATLAB】


2、文章：Deep retinex decomposition for low-light enhancement 【Retinex】

Cite as: Wei C, Wang W, Yang W, et al. Deep retinex decomposition for low-light enhancement[J]. arXiv preprint arXiv:1808.04560, 2018.

Paper: https://arxiv.org/abs/1808.04560

Code: https://github.com/weichen582/RetinexNet【TensorFlow】


3、文章：MBLLEN: Low-light image/video enhancement using CNNs

Cite as: Lv F, Lu F, Wu J, et al. MBLLEN: Low-Light Image/Video Enhancement Using CNNs[C]//BMVC. 2018: 220.

Paper: http://bmvc2018.org/contents/papers/0700.pdf

Code: https://github.com/Lvfeifan/MBLLEN【TensorFlow】


4、文章：Learning a deep single image contrast enhancer from multi-exposure images

Cite as: 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: https://ieeexplore.ieee.org/abstract/document/8259342/

Code: https://github.com/csjcai/SICE【Caffe & MATLAB】


5、文章：Learning to see in the dark

Cite as: 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: https://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Learning_to_See_CVPR_2018_paper.html

Code: https://github.com/cchen156/Learning-to-See-in-the-Dark【TensorFlow】


6、文章：DeepExposure: Learning to expose photos with asynchronously reinforced adversarial learning

Cite as: Yu R, Liu W, Zhang Y, et al. Deepexposure: Learning to expose photos with asynchronously reinforced adversarial learning[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018: 2153-2163.

Paper: https://dl.acm.org/doi/abs/10.5555/3326943.3327142

Code: 未开源【TensorFlow】

【2017】

1、文章：LLNet: A deep autoencoder approach to natural low-light image enhancement

Cite as: 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: https://www.sciencedirect.com/science/article/abs/pii/S003132031630125X

Code: https://github.com/kglore/llnet_color【Theano】

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