资源帖- low-light image enhancement 论文代码数据集

Posted 2023-01-11 sereasuesue

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Datasets

dataset	brief introduction	link
VIP-LowLight	Eight Natural Images Captured in Very Low-Light Conditions	https://uwaterloo.ca/vision-image-processing-lab/research-demos/vip-lowlight-dataset
ReNOIR	RENOIR - A Dataset for Real Low-Light Image Noise Reduction	http://ani.stat.fsu.edu/~abarbu/Renoir.html
Raw Image Low-Light Object	-	https://wiki.qut.edu.au/display/cyphy/Datasets
SID	Learning to see in the dark; light level (outdoor scene 0.2 lux - 5 lux; indoor scene: 0.03 lux - 0.3 lux)	http://vladlen.info/publications/learning-see-dark （including codes）
ExDARK	Getting to Know Low-light Images with The Exclusively Dark Dataset	https://github.com/cs-chan/Exclusively-Dark-Image-Dataset （including codes）
MIT-Adobe FiveK	Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs (with ~4% low light images)	https://data.csail.mit.edu/graphics/fivek
LRAICE-Dataset	A Learning-to-Rank Approach for Image Color Enhancement	-
The 500px Dataset	Exposure: A White-Box Photo Post-Processing Framework	-
DPED	DSLR-quality photos on mobile devices with deep convolutional networks	http://people.ee.ethz.ch/~ihnatova
LOL	Deep Retinex Decomposition for Low-Light Enhancement	https://daooshee.github.io/BMVC2018website
VV - most challenging cases	Busting image enhancement and tone-mapping algorithms: A collection of the most challenging cases from Vassilios Vonikakis	https://sites.google.com/site/vonikakis/datasets/challenging-dataset-for-enhancement
VV - Phos	A color image database of 15 scenes captured under different illumination conditions from Vassilios Vonikakis	http://robotics.pme.duth.gr/phos2.html
SICE	A large-scale multi-exposure image dataset, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images	https://github.com/csjcai/SICE
The Extended Yale Face Database B	The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions.	http://vision.ucsd.edu/~iskwak/ExtYaleDatabase/ExtYaleB.html
the nighttime image dataset	A dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms	http://mlg.idm.pku.edu.cn/

2020

Meng et al, GIA-Net: Global Information Aware Network for Low-light Imaging. [paper][code]
Kwon et al, DALE : Dark Region-Aware Low-light Image Enhancement. [paper][code]
Yang et al, From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement. [paper][code]
Atoum et al, Color-wise Attention Network for Low-light Image Enhancement. [paper][code]
Lv et al, Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset. [paper][code]
Guo et al, Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. [paper][code]
Wei et al, A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising. [paper][code]
Fu et al, Learning an Adaptive Model for Extreme Low-light Raw Image Processing. [paper][code]
Wang et al, Extreme Low-Light Imaging with Multi-granulation Cooperative Networks. [paper][code]
Karadeniz et al, Burst Denoising of Dark Images. [paper][code]
Xiong et al, Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks. [paper][code]
Liang et al, Deep Bilateral Retinex for Low-Light Image Enhancement. [paper][code]
Zhang et al, ATTENTION-BASED NETWORK FOR LOW-LIGHT IMAGE ENHANCEMENT. [paper][code]
Li et al, Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement. [paper][code]
Zhang et al, Self-supervised Image Enhancement Network: Training with Low Light Images Only. [paper][code]
Xu et al, Learning to Restore Low-Light Images via Decomposition-and-Enhancement. [paper][code]

2019

Wang et al, Underexposed Photo Enhancement using Deep Illumination Estimation. [paper][code]
Loh et al, Low-light image enhancement using Gaussian Process for features retrieval. [paper][code]
Zhang et al, Kindling the Darkness: A Practical Low-light Image Enhancer. [paper][code]
Ren et al, Low-Light Image Enhancement via a Deep Hybrid Network. [paper][code]
Jiang et al, EnlightenGAN: Deep Light Enhancement without Paired Supervision. [paper][code]
Wang et al, RDGAN: RETINEX DECOMPOSITION BASED ADVERSARIAL LEARNING FOR LOW-LIGHT ENHANCEMENT. [paper][code]

2018

Chen et al, Learning to See in the Dark. [paper][code]
Wei et al, Deep Retinex Decomposition for Low-Light Enhancement. [paper][code]
Wang et al, GLADNet: Low-Light Enhancement Network with Global Awareness. [paper][code]
Lv et al, MBLLEN: Low-light Image/Video Enhancement Using CNNs. [paper][code]
Jiang et al, Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways. [paper][code]
Cai et al, Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images. [paper][code]

2017

GHARBI et al, Deep Bilateral Learning for Real-Time Image Enhancement. [paper][code]
Shen et al, MSR-net:Low-light Image Enhancement Using Deep Convolutional Network. [paper][code]
Tao et al, LLCNN: A convolutional neural network for low-light image enhancement. [paper][code]
Ying et al, A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement. [paper][code]

2016

Lore et al, LLNet: A Deep Autoencoder approach to Natural Low-light Image Enhancement. [paper][code]
Guo et al, LIME: Low-Light Image Enhancement via Illumination Map Estimation. [paper][code]