|paper|name|Real world|resolution|des|link|
|-|-|-|-|-|-|-|-|
|Underexposed Photo Enhancement using Deep Illumination Estimation|3000pairs UPE |T|6000x4000| 版权问题没公开,gt人工ps产生 2750for training rest for testing|https://github.com/wangruixing/DeepUPE｜
|SICE:Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images|589多曝光序列共4413images，3-18images per sequence |T|6000x4000，3000x2000|不对齐！|https://github.com/csjcai/SICE|
|Learning to See in the Dark|SID RAW->RGB,5094RAW|T|4240×2832 for Sony and 6000×4000 for the Fuji| 70% for training,20% for testing,10%for vali|https://github.com/cchen156/Learning-to-See-in-the-Dark|
|待定Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise||F|-||
|Kindling the Darkness:A Practical Low-light Image Enhancer|提到了没有gt的数据集：LIME，NPE，MEF|
|Low-Light Image Enhancement via a Deep Hybrid Network||T||336 paires selected from the MIT-Adobe FiveK dataset没开源|
|Deep Retinex Decomposition for Low-Light Enhancement|LOL 500pairs|T|400x600|485for training 15for eval|https://daooshee.github.io/BMVC2018website/|
|MSR-net:Low-light Image Enhancement Using Deep Convolutional Network|没开源10000pairs from 1000 HQ image after PS ,1HQ->lowLight 提到了real-world llimages MEF,NPE,VV|F|64x64|8000for training 2000for testing|
|MBLLEN: Low-light Image/Video Enhancement Using CNNs|Image Dataset(gamma adjustment+PossionNoise)|F|from VOC |16925 images in the VOC dataset to synthesize the training set, 56 images for the validation set, and 144 images for the test set|http://phi-ai.org/project/MBLLEN/default.htm|
|MBLLEN: Low-light Image/Video Enhancement Using CNNs|Video Dataset (from --VDS)|F|video clips:31x255x255x3|20000 samples,95%for training ,the rest for test|http://phi-ai.org/project/MBLLEN/default.htm|
|Attention-guided Low-light Image Enhancement||F||randomly select 1% of them as the test set which contains 965 images. In this paper, we use the data-balanced subset including 22656 images as the training set|http://phi-ai.org/project/AgLLNet/default.htm|