Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Last update: Jan 03, 2023

Overview

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

This repository provides a unified online platform, LoLi-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, LoLi-Phone (will be released soon), in which the images and videos are taken by various phones' cameras under diverse illumination conditions and scenes, and 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: Lighting the Darkness in the Deep Learning Era. 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 LoLi-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!

LoLi-Platform
LoLi-Phone Dataset
Methods
Datasets
Metrics
Citation

LoLi-Platform

Currently, the LoLi-Platform covers 13 popular deep learning-based LLIE methods including LLNet, LightenNet, Retinex-Net, EnlightenGAN, MBLLEN, KinD, KinD++, TBEFN, DSLR, DRBN, ExCNet, Zero-DCE, and RRDNet, where the results of any inputs can be produced through a user-friendly web interface. Have fun: LoLi-Platform.

LoLi-Phone

LoLi-Phone dataset contains 120 videos (55,148 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 LoLi-Phone dataset.

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.		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

Datasets

Abbreviation	Number	Format	Real/Synetic	Video	Paired/Unpaired/Application	Dataset
LOL paper	500	RGB	Real	No	Paired	Dataset
SCIE paper	4413	RGB	Real	No	Paired	Dataset
MIT-Adobe FiveK paper	5000	Raw	Real	No	Paired	Dataset
SID paper	5094	Raw	Real	No	Paired	Dataset
DRV paper	202	Raw	Real	Yes	Paired	Dataset
SMOID paper	179	Raw	Real	Yes	Paired	Dataset
LIME paper	10	RGB	Real	No	Unpaired	Dataset
NPE paper	84	RGB	Real	No	Unpaired	Dataset
MEF paper	17	RGB	Real	No	Unpaired	Dataset
DICM paper	64	RGB	Real	No	Unpaired	Dataset
VV	24	RGB	Real	No	Unpaired	Dataset
ExDARK paper	7363	RGB	Real	No	Application	Dataset
BBD-100K paper	10,000	RGB	Real	Yes	Application	Dataset
DARK FACE paper	6000	RGB	Real	No	Application	Dataset

Metrics

Abbreviation	Full-/Non-Reference	Platform	Code
MAE (Mean Absolute Error)	Full-Reference
MSE (Mean Square Error)	Full-Reference
PSNR (Peak Signal-to-Noise Ratio)	Full-Reference
SSIM (Structural Similarity Index Measurement)	Full-Reference	MATLAB	Code
LPIPS (Learned Perceptual Image Patch Similarity)	Full-Reference	PyTorch	Code
LOE (Lightness Order Error)	Non-Reference	MATLAB	Code
NIQE (Naturalness Image Quality Evaluator)	Non-Reference	MATLAB	Code
PI (Perceptual Index)	Non-Reference	MATLAB	Code
SPAQ (Smartphone Photography Attribute and Quality)	Non-Reference	PyTorch	Code
NIMA (Neural Image Assessment)	Non-Reference	PyTorch/TensorFlow	Code/Code

Citation

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

@article{LoLi,
  title={Lighting the Darkness in the Deep Learning Era},
  author={Li, Chongyi and Guo, Chunle and Han, Linghao and Jiang, Jun and Cheng, Ming-Ming and Gu, Jinwei and Loy, Chen Change},
  journal={arXiv:2104.10729},
  year={2021}
}

Contact Information

[email protected]

[email protected]

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Related tags

Overview

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Contents

LoLi-Platform

LoLi-Phone

Methods

Datasets

Metrics

Citation

Owner

Chongyi Li

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