By Xiang Chen, Yufeng Li, Yufeng Huang

• A collection of awesome low-light image enhancement methods. Papers, codes and datasets are maintained.

• VV [download]
• SID [download]
• LOL [download]
• NPE [download]
• MEF [download]
• CID [download]
• SCIE [download]
• LIME [download]
• DICM [download]
• BIMEF [download]
• ExDark [download]
• MIT-Adobe FiveK [download]

• 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]

• 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]

• 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]

• 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]

• 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]

• MSE (Mean Square Error)
• LOE (Lightness Order Error) [matlab code]
• VIF (Visual Quality) [matlab code]
• PSNR (Peak Signal-to-Noise Ratio) [matlab code] [python code]
• SSIM (Structural Similarity) [matlab code] [python code]
• FSIM (Feature Similarity) [matlab code]
• NIQE (Naturalness Image Quality Evaluator) [matlab code][python code]
• PIQE (Perception based Image Quality Evaluator) [matlab code]
• BRISQUE (Blind Image Spatial Quality Evaluator) [matlab code]

• The above content is constantly updated, welcome continuous attention!

• If you have any question, please feel free to contact me.