Low-Light Image and Video Enhancement Using Deep Learning: A Survey

This repository provides：

1) a unified online platform, LLIE-Platform http://mc.nankai.edu.cn/ll/, that covers many popular deep learning-based LLIE methods, of which the results can be produced through a user-friendly web interface, contains a low-light image and video dataset.

2) a new dataset LLIV-Phone https://drive.google.com/file/d/1QS4FgT5aTQNYy-eHZ_A89rLoZgx_iysR/view?usp=sharing, in which the images and videos are taken by various phones' cameras under diverse illumination conditions and scenes, and

3) collects deep learning-based low-light image and video enhancement methods, datasets, and evaluation metrics.

More content and details can be found in our Survey Paper: Low-Light Image and Video Enhancement Using Deep Learning: A Survey.

我们提供中文翻译版：基于深度学习的低照度图像与视频增强综述.

We provide the comparison results on the real low-light videos taken by different mobile phones’ cameras at YouTube https://www.youtube.com/watch?v=Elo9TkrG5Oo&t=6s.

✌We will periodically update the content. Welcome to let us know if we miss your work that is published in top-tier Journal or conference. We will add it.

✌Our LLIE-Platform supports the function of download. Please right click and then save the figure.

✌ If you use this dataset or platform, please cite our paper. Please hit the star at the top-right corner. Thanks!

✌ Please hit the star in the repo when you ask for any code. Thanks!

📣News

1. The survey is accepted by TPAMI.

2. We newly add the Zero-DCE++ to the LLIE-Platform. Have Fun!

Zero-DCE++: C. Li, C. Guo, and C. C. Loy, Learning to enhance low-light image via zero-reference deep curve estimation, TPAMI, 2021.

🌱Contents

1. LLIE-Platform
2. LLIV-Phone Dataset
3. Methods
4. Datasets
5. Metrics
6. Citation

📋LoLi-Platform

Currently, the LLIE-Platform covers 14 popular deep learning-based LLIE methods including LLNet, LightenNet, Retinex-Net, EnlightenGAN, MBLLEN, KinD, KinD++, TBEFN, DSLR, DRBN, ExCNet, Zero-DCE, Zero-DCE++, and RRDNet, where the results of any inputs can be produced through a user-friendly web interface. Have fun: LLIE-Platform.

📋LLIV-Phone

LLIV-Phone dataset contains 120 videos (45,148 images) taken by 18 different phones' cameras including iPhone 6s, iPhone 7, iPhone7 Plus, iPhone8 Plus, iPhone 11, iPhone 11 Pro, iPhone XS, iPhone XR, iPhone SE, Xiaomi Mi 9, Xiaomi Mi Mix 3, Pixel 3, Pixel 4, Oppo R17, Vivo Nex, LG M322, OnePlus 5T, Huawei Mate 20 Pro under diverse illumination conditions (e.g., weak illumination, underexposure, dark, extremely dark, back-lit, non-uniform light, color light sources, etc.) in the indoor and outdoor scenes.

Anyone can access the LLIV-Phone dataset via

Google Drive: https://drive.google.com/file/d/1QS4FgT5aTQNYy-eHZ_A89rLoZgx_iysR/view?usp=sharing or

Baidu Cloud：https://pan.baidu.com/s/1-8PF3dfbtlHlmk9y5ZKx_w, Password: s0b9)

📋Methods

	Date	Publication	Title	Abbreviation	Code	Platform
2017	PR	LLNet: A deep autoencoder approach to natural low-light image enhancement paper	LLNet	Code	Theano
2018	PRL	LightenNet: A convolutional neural network for weakly illuminated image enhancement paper	LightenNet	Code	Caffe & MATLAB
2018	BMVC	Deep retinex decomposition for low-light enhancement paper	Retinex-Net	Code	TensorFlow
2018	BMVC	MBLLEN: Low-light image/video enhancement using CNNs paper	MBLLEN	Code	TensorFlow
2018	TIP	Learning a deep single image contrast enhancer from multi-exposure images paper	SCIE	Code	Caffe & MATLAB
2018	CVPR	Learning to see in the dark paper	Chen et al.	Code	TensorFlow
2018	NeurIPS	DeepExposure: Learning to expose photos with asynchronously reinforced adversarial learning paper	DeepExposure		TensorFlow
2019	ICCV	Seeing motion in the dark paper	Chen et al.	Code	TensorFlow
2019	ICCV	Learning to see moving object in the dark paper	Jiang and Zheng	Code	TensorFlow
2019	CVPR	Underexposed photo enhancement using deep illumination estimation paper	DeepUPE	Code	TensorFlow
2019	ACMMM	Kindling the darkness: A practical low-light image enhancer paper	KinD	Code	TensorFlow
2019	ACMMM (IJCV)	Kindling the darkness: A practical low-light image enhancer paper (Beyond brightening low-light images paper)	KinD (KinD++)	Code	TensorFlow
2019	ACMMM	Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement paper	Wang et al.		Caffe
2019	TIP	Low-light image enhancement via a deep hybrid network paper	Ren et al.		Caffe
2019(2021)	arXiv(TIP)	EnlightenGAN: Deep light enhancement without paired supervision paper arxiv	EnlightenGAN	Code	PyTorch
2019	ACMMM	Zero-shot restoration of back-lit images using deep internal learning paper	ExCNet	Code	PyTorch
2020	CVPR	Zero-reference deep curve estimation for low-light image enhancement paper	Zero-DCE	Code	PyTorch
2020	CVPR	From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement paper	DRBN	Code	PyTorch
2020	ACMMM	Fast enhancement for non-uniform illumination images using light-weight CNNs paper	Lv et al.		TensorFlow
2020	ACMMM	Integrating semantic segmentation and retinex model for low light image enhancement paper	Fan et al.
2020	CVPR	Learning to restore low-light images via decomposition-and-enhancement paper	Xu et al.	Code	PyTorch
2020	AAAI	EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network paper	EEMEFN		PyTorch
2020	TIP	Lightening network for low-light image enhancement paper	DLN		PyTorch
2020	TMM	Luminance-aware pyramid network for low-light image enhancement paper	LPNet		PyTorch
2020	ECCV	Low light video enhancement using synthetic data produced with an intermediate domain mapping paper	SIDGAN		TensorFlow
2020	TMM	TBEFN: A two-branch exposure-fusion network for low-light image enhancement paper	TBEFN	Code	TensorFlow
2020	ICME	Zero-shot restoration of underexposed images via robust retinex decomposition paper	RRDNet	Code	PyTorch
2020	TMM	DSLR: Deep stacked laplacian restorer for low-light image enhancement paper	DSLR	Code	PyTorch
2021	TPAMI	Learning to enhance low-light image via zero-reference deep curve estimation paper	Zero-DCE++	Code	PyTorch
2021	CVPR	Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement paper	RUAS	Code	PyTorch
2021	CVPR	Learning temporal consistency for low light video enhancement from single images paper	Zhang et al.	Code	PyTorch
2021	CVPR	Nighttime visibility enhancement by increasing the dynamic range and suppression of light effects paper	Sharma and Tan
2021	TCSVT	RetinexDIP: A unified deep framework for low-light image enhancement paper	RetinexDIP	Code	PyTorch
2021	TIP	Sparse gradient regularized deep retinex network for robust low-light image enhancement paper	Retinex-Net		PyTorch
2021	TCSVT	Low-light image enhancement via progressive-recursive network paper	PRIEN		PyTorch
2021	TIP	Band representation-based semi-supervised low-light image enhancement: Bridging the gap between signal fidelity and perceptual quality paper	DRBN		PyTorch
2021	ICCV	Adaptive unfolding total variation network for low-light image enhancement paper	UTVNet	Code	PyTorch
2021	ICCV	Seeing dynamic scene in the dark: A high-quality video dataset with mechatronic alignment paper	Wang et al.	Code	PyTorch
2022	CVPR	SNR-aware Low-Light Image Enhancement paper	SNR-Aware	Code	PyTorch
2022	CVPR	Toward Fast, Flexible, and Robust Low-Light Image Enhancement paper	SCI	Code	PyTorch
2022	CVPR	URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement paper	URetinex-Net	Code	PyTorch

📋Datasets

📋Metrics

📜License

The code, platform, and dataset are made available for academic research purpose only.

📚Citation

If you find the repository helpful in your resarch, please cite the following paper.

@article{LoLi,
  title={Low-Light Image and Video Enhancement Using Deep Learning: A Survey},
  author={Li, Chongyi and Guo, Chunle and Han, Linghao and Jiang, Jun and Cheng, Ming-Ming and Gu, Jinwei and Loy, Chen Change},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021}
}

📋Paper

Official Version

arXiv Version

Chinese Version

📭Contact

lichongyi25@gmail.com; guochunle@nankai.edu.cn