Compilation of articles on low-light image enhancement
Low-light image enhancement is an important part of the image enhancement task, and the current collection of low-light image enhancement methods is uneven. Therefore, I hope to summarize the existing low-light image enhancement algorithms (articles and codes) based on existing articles. I hope to provide some convenience for myself and everyone in finding articles and codes in the field of low-light image enhancement.
Websites used often
First, I will introduce some more suitable URLs. The first three are URLs on github that organize low-light image enhancement, and the fourth one is a link on the paperswithcode website about organizing low-light images.
- https://github.com/Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open
- https://github.com/baidut/OpenCE
- https://github.com/dawnlh/low-light-image-enhancement-resources
- https://paperswithcode.com/task/low-light-image-enhancement
Commonly used data sets
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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. -
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. -
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. -
VV: https://drive.google.com/drive/folders/1lp6m5JE3kf3M66Dicbx5wSnvhxt90V4T
-
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. -
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. -
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. -
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.
Paper list
【2022】
1. Article: DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations [Unsupervised Learning] [User-defined Enhancement]
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. Article: EnlightenGAN: Deep Light Enhancement Without Paired Supervision [Semi-supervised Learning] [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. Article: Beyond Brightening Low-light Images [Supervised Learning] [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.
Code: https://github.com/Li-Chongyi/Zero-DCE [Pytorch] 2. Article: From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement [semi-supervised learning]
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.
Code: https://github.com/flyywh/CVPR-2020-Semi-Low-Light [Pytorch] 3. Article: From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement [Semi-supervised learning]
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.
Code: https://github.com/flyywh/CVPR-2020-Semi-Low-Light 【Pytorch】
4. Article: Fast enhancement for non-uniform illumination images using light-weight CNNs [supervised learning]
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: Not open source [TensorFlow] 5. Article: 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: Not open source 6. Article: 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.
Code: Not open source [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: Not open source [Pytorch] 8. Article: 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: Not open source [Pytorch] 9. Article: 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: Not open source [Tensorflow] 11. Article: 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.
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.
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.
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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