├── DerainDatasets.md └── README.md /DerainDatasets.md: -------------------------------------------------------------------------------- 1 | # DerainDatasets 2 | * Synthetic rainy datasets for single image deraining are collected here. 3 | 4 | # Single Image Datasets 5 | ## Synthetic Datasets 6 | 7 | * [Rain12_CVPR2016](http://yu-li.github.io/paper/li_cvpr16_rain.zip) in [LP](https://ieeexplore.ieee.org/document/7780668/) 8 | 9 | * [Rain100L_CVPR2017](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html) in [JORDER](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf) 10 | 11 | * [Rain100H_CVPR2017 new version](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html) in [JORDER](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf) 12 | * **Note that Rain100H provided by Yang et al. is updated in their [website](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html). Original version contains 1800 clean/rainy training image pairs and 100 clean/rainy testing image pairs. The new version contains 1800 clean/rainy training image pairs and 200 clean/rainy testing image pairs. All the methods assess their performances on original version instead of the new one. As a result, the metrics will be different when you retrain the models using the new one.** 13 | 14 | * [Rain100H_CVPR2017 old version](): 15 | * train set 16 | * Link: https://pan.baidu.com/s/1uiTz_ME_9Lq5k0yA6c7ZEw 17 | * Extraction code: x3gy 18 | * test set 19 | * Link: https://pan.baidu.com/s/1GLLpNJ8P4mkwfX7meMphTg 20 | * Extraction code: axpq 21 | 22 | * [Rain800_Arxiv2017](https://github.com/hezhangsprinter/ID-CGAN) in [ID_CGAN](https://arxiv.org/abs/1701.05957) 23 | 24 | * [Rain1400_CVPR2017](https://xueyangfu.github.io/projects/cvpr2017.html) in [DDN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Removing_Rain_From_CVPR_2017_paper.pdf) 25 | 26 | * [Rain1200_CVPR2018](https://github.com/hezhangsprinter/DID-MDN) in [DID_MDN](https://arxiv.org/abs/1802.07412) 27 | 28 | * [SPANet Dataset__CVPR2019](https://stevewongv.github.io/derain-project.html) in [SPANet](https://arxiv.org/pdf/1904.01538.pdf) 29 | 30 | * [Heavy Rainy Dataset_CVPR2019](https://drive.google.com/file/d/1rFpW_coyxidYLK8vrcfViJLDd-BcSn4B/view) in [Heavy Rain Image Restoration](http://export.arxiv.org/pdf/1904.05050) 31 | 32 | ## Real-World Datasets 33 | 34 | * [Practical](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html) in [JORDER](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf) 35 | 36 | * [Practical](https://github.com/hezhangsprinter/ID-CGAN) in [ID_CGAN](https://arxiv.org/abs/1701.05957) 37 | 38 | # Synthetic Methods 39 | 40 | * [Photoshop](https://www.photoshopessentials.com/photo-effects/rain/) 41 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DerainZoo (Single Image vs. Video Based) 2 | [Youzhao Yang](https://github.com/nnuyi), [Hong Lu](http://homepage.fudan.edu.cn/honglu/machine-vision-lab/) in [Fudan Machine Vision Lab](https://github.com/FudanMV) 3 | 4 | ## 1 Description 5 | * DerainZoo: A list of deraining methods. Papers, codes and datasets are maintained. Other sources about deraining can be observed in [web1](https://github.com/TaiXiangJiang/FastDeRain) and [web2](https://github.com/hongwang01/Video-and-Single-Image-Deraining). 6 | 7 | * Datasets for single image deraining are available at the [website](https://github.com/nnUyi/DerainZoo/blob/master/DerainDatasets.md). 8 | 9 | * More datasets about other image processing task (brightening, HDR, color enhancement, and inpainting) are available [here](https://github.com/nnUyi/Image-Processing-Datasets). 10 | 11 | ## 2 Image Quality Metrics 12 | * PSNR (Peak Signal-to-Noise Ratio) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4550695) [[matlab code]](https://www.mathworks.com/help/images/ref/psnr.html) [[python code]](https://github.com/aizvorski/video-quality) 13 | * SSIM (Structural Similarity) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1284395) [[matlab code]](http://www.cns.nyu.edu/~lcv/ssim/ssim_index.m) [[python code]](https://github.com/aizvorski/video-quality/blob/master/ssim.py) 14 | * VIF (Visual Quality) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1576816) [[matlab code]](http://sse.tongji.edu.cn/linzhang/IQA/Evalution_VIF/eva-VIF.htm) 15 | * FSIM (Feature Similarity) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5705575) [[matlab code]](http://sse.tongji.edu.cn/linzhang/IQA/FSIM/FSIM.htm) 16 | * NIQE (Naturalness Image Quality Evaluator) [[paper]](http://live.ece.utexas.edu/research/Quality/niqe_spl.pdf)[[matlab code]](http://live.ece.utexas.edu/research/Quality/index_algorithms.htm)[[python code]](https://github.com/aizvorski/video-quality/blob/master/niqe.py) 17 | 18 | **Image & Video Quality Assessment Algorithms [[software release]](http://live.ece.utexas.edu/research/Quality/index_algorithms.htm)[[Texas Lab]](http://live.ece.utexas.edu/research/quality/)** 19 | 20 | ## 3 Single Image Deraining 21 | ### 3.1 Datasets 22 | ------------ 23 | #### 3.1.1 Synthetic Datasets 24 | * Rain12 [[paper](https://ieeexplore.ieee.org/document/7780668/)] [[dataset](http://yu-li.github.io/paper/li_cvpr16_rain.zip)] (2016 CVPR) 25 | * Rain100L_old_version [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf)][[dataset](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html)](2017 CVPR) 26 | * Rain100L_new_version [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf)][[dataset](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html)] 27 | * Rain100H_old_version [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf)][[dataset](https://github.com/nnUyi/DerainZoo/blob/master/DerainDatasets.md)](2017 CVPR) 28 | * Rain100H_new_version [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf)][[dataset](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html)] 29 | * Rain800 [[paper](https://arxiv.org/abs/1701.05957)][[dataset](https://github.com/hezhangsprinter/ID-CGAN)] (2017 Arxiv) 30 | * Rain1200 [[paper](https://arxiv.org/abs/1802.07412)][[dataset](https://github.com/hezhangsprinter/DID-MDN)] (2018 CVPR) 31 | * Rain1400 [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Removing_Rain_From_CVPR_2017_paper.pdf)][[dataset](https://xueyangfu.github.io/projects/cvpr2017.html)] (2017 CVPR) 32 | * Heavy Rain Dataset [[paper](http://export.arxiv.org/pdf/1904.05050)][[dataset](https://drive.google.com/file/d/1rFpW_coyxidYLK8vrcfViJLDd-BcSn4B/view)] (2019 CVPR) 33 | 34 | #### 3.1.2 Real-world Datasets 35 | * Practical_by_Yang [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf)][[dataset](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html)] (2017 CVPR) 36 | * Practica_by_Zhang [[paper](https://arxiv.org/abs/1701.05957)][[dataset](https://github.com/hezhangsprinter/ID-CGAN)] (2017 Arxiv) 37 | * Real-world Paired Rain Dataset [[paper](https://arxiv.org/pdf/1904.01538.pdf)][[dataset](https://stevewongv.github.io/derain-project.html)] (2019 CVPR) 38 | 39 | ### 3.2 Papers 40 | -------------- 41 | ### 2021 42 | * NR [[paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Xiao_Improving_De-Raining_Generalization_via_Neural_Reorganization_ICCV_2021_paper.pdf)][code][web] 43 | * Fu, Xueyang etc. Improving De-raining Generalization via Neural Reorganization. (ICCV 2021) 44 | * DerainRLNet [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Robust_Representation_Learning_With_Feedback_for_Single_Image_Deraining_CVPR_2021_paper.pdf)][[code](https://github.com/LI-Hao-SJTU/DerainRLNet)][[web](https://github.com/LI-Hao-SJTU)] 45 | * Chen, Chenghao etc. Robust Representation Learning with Feedback for Single Image Deraining. (CVPR 2021) 46 | 47 | * VRGNet [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_From_Rain_Generation_to_Rain_Removal_CVPR_2021_paper.pdf)][[code](https://github.com/hongwang01/VRGNet)][[web](https://github.com/hongwang01)] 48 | * Wang, Hong etc. From Rain Generation to Rain Removal. (CVPR 2021) 49 | 50 | * IDCL [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhou_Image_De-Raining_via_Continual_Learning_CVPR_2021_paper.pdf)][[code]()][[web]()] 51 | * Zhou, Man etc. Image De-raining via Continual Learning. (CVPR 2021) 52 | 53 | * DRG [[paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Yue_Semi-Supervised_Video_Deraining_With_Dynamical_Rain_Generator_CVPR_2021_paper.pdf)][[code](https://github.com/zsyOAOA/S2VD)][[web](https://github.com/zsyOAOA)] 54 | * Yue, Zongsheng etc. Semi-Supervised Video Deraining with Dynamical Rain Generator. (CVPR 2021) 55 | 56 | * MPRNet [[paper](https://arxiv.org/abs/2102.02808)][[code](https://github.com/swz30/MPRNet)][[web](https://github.com/swz30)] 57 | * Zamir et al. Multi-Stage Progressive Image Restoration. (CVPR 2021) 58 | 59 | * ADN [paper][[code](https://github.com/nnUyi/ADN)][[web](https://github.com/nnUyi)] 60 | * Yang, Youzhao etc. A Fast And Eefficient Network for Single Image Deraining. (ICASSP 2021) 61 | 62 | * DualGCN [[paper](https://xueyangfu.github.io/paper/2021/AAAI/Preprint.pdf)][[code](https://xueyangfu.github.io/paper/2021/AAAI/code.zip)][[web](https://xueyangfu.github.io)] 63 | * Fu, Xueyang etc. Rain Streak Removal via Dual Graph Convolutional Network. (AAAI 2021) 64 | 65 | * IPT [[paper](https://arxiv.org/pdf/2012.00364.pdf)][[code]()][[web]()] 66 | * Chen, Hanting etc. Pre-Trained Image Processing Transformer. (Arxiv 2020) 67 | 68 | ### 2020 69 | * WDNet [[paper](https://arxiv.org/pdf/2008.00823.pdf)][[code]()][[web]()] 70 | * Liu, Lin etc. Wavelet-Based Dual-Branch Network for Image Demoir´eing. (2020 ECCV) 71 | 72 | * Rethinking Image Deraining [[paper](https://arxiv.org/pdf/2008.00823.pdf)][[code](https://github.com/yluestc/derain)][[web](https://github.com/yluestc)] 73 | * Wang, Yinglong etc. Rethinking Image Deraining via Rain Streaks and Vapors. (2020 ECCV) 74 | 75 | * JDNet [[paper](https://arxiv.org/pdf/2008.02763.pdf)][[code](https://github.com/Ohraincu/JDNet)][[web](https://github.com/Ohraincu)] 76 | * Wang, Cong etc. Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network. (2020 ACMMM) 77 | 78 | * DCSFN [[paper](https://arxiv.org/pdf/2008.00767.pdf)][[code]( https://github.com/Ohraincu/DCSFN)][[web](https://github.com/Ohraincu)] 79 | * Wang, Cong etc. DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal. (2020 ACMMM) 80 | 81 | * CVID [[paper](https://arxiv.org/pdf/2004.11373.pdf)][[code](https://github.com/Yingjun-Du/VID)][[web](https://github.com/Yingjun-Du)] 82 | * Du, Yingjun etc. Conditional Variational Image Deraining. (2020 TIP) 83 | 84 | * DRD-Net [[paper]()][[web](https://github.com/Dengsgithub)][[code](https://github.com/Dengsgithub/DRD-Net)] 85 | * Deng, Sen etc. Detail-recovery Image Deraining via Context Aggregation Networks. (2020 CVPR) 86 | 87 | * RCDNet [[paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_A_Model-Driven_Deep_Neural_Network_for_Single_Image_Rain_Removal_CVPR_2020_paper.pdf)][[web]()][[code](https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Wang_A_Model-Driven_Deep_CVPR_2020_supplemental.pdf)] 88 | * Wang, Hong etc. A Model-driven Deep Neural Network for Single Image Rain Removal. (2020 CVPR) 89 | 90 | * Syn2Rel [[paper](https://arxiv.org/pdf/2006.05580.pdf)][[web](https://github.com/rajeevyasarla)][[code](https://github.com/rajeevyasarla/Syn2Real)] 91 | * Rajeev Yasarla et al. Syn2Real Transfer Learning for Image Deraining using Gaussian Processes. (2020 CVPR) 92 | 93 | * MSPFN [[paper](https://arxiv.org/pdf/2003.10985.pdf)][[code](https://github.com/kuihua/MSPFN)][[web](https://github.com/kuihua)] 94 | * Jiang Kui et al. Multi-Scale Progressive Fusion Network for Single Image Deraining. (2020 CVPR) 95 | 96 | * Physical Model Guided ID [[paper](https://arxiv.org/pdf/2003.13242.pdf)][[code](https://supercong94.wixsite.com/supercong94)][[web](https://supercong94.wixsite.com/supercong94)] 97 | * Cong Wang et al. Physical Model Guided Deep Image Deraining. (2020 ICME) 98 | 99 | * RDDAN [[paper](https://ieeexplore.ieee.org/abstract/document/9102945/)][[code](https://github.com/nnUyi/RDDAN)][[website](https://github.com/nnUyi)] 100 | * Yang, Youzhao et al. RDDAN: A Residual Dense Dilated Aggregated Network for Single Image Deraining. (2020 ICME) 101 | 102 | * DiG-CoM [[paper](https://www.computer.org/csdl/proceedings-article/icme/2020/09102800/1kwqO6toxQk)][[code](https://github.com/nnUyi/DiG-CoM)][[website](https://github.com/nnUyi)] 103 | * Ran, Wu; Yang, Youzhao et al. Single Image Rain Removal Boosting via Directional Gradient. (2020 ICME) 104 | 105 | * VID [[paper](http://openaccess.thecvf.com/content_WACV_2020/papers/Du_Variational_Image_Deraining_WACV_2020_paper.pdf)][code][[web](https://csjunxu.github.io/)] 106 | * Xu, Jun et al. Variational Image Deraining. (2020 WACV) 107 | 108 | * CMGD [[paper](https://ieeexplore.ieee.org/abstract/document/9007569)][code][[web](https://github.com/rajeevyasarla)] 109 | * Rajeev Yasarla et al. Confidence Measure Guided Single Image De-Raining. (2020 TIP) 110 | 111 | ### 2019 112 | * Survey [[paper](https://arxiv.org/pdf/1912.07150.pdf)][code][web] 113 | * Yang, Wenhan et al. Single Image Deraining: From Model-Based to Data-Driven and Beyond. (2019 TPAMI) 114 | 115 | * RWL [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8610325)][code][web] 116 | * Yang, Wenhan et al. Scale-Free Single Image Deraining Via VisibilityEnhanced Recurrent Wavelet Learning. (2019 TIP) 117 | 118 | * DPRDN [[paper](https://arxiv.org/pdf/1908.10521.pdf)][code][web] 119 | * Wei, Yanyan et al. A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining. (2019 ICDM) 120 | 121 | * Survey [[paper](https://arxiv.org/pdf/1909.08326.pdf)][[code](https://github.com/hongwang01/Video-and-Single-Image-Deraining)][web] 122 | * Wang, Hong et al. A Survey on Rain Removal from Video and Single Image. (2019 Arxiv) 123 | 124 | * ERL-Net [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_ERL-Net_Entangled_Representation_Learning_for_Single_Image_De-Raining_ICCV_2019_paper.pdf)][[code](https://github.com/RobinCSIRO/ERL-Net-for-Single-Image-Deraining)][web] 125 | * Wang, Guoqing et al. ERL-Net: Entangled Representation Learning for Single Image De-Raining. (2019 ICCV) 126 | 127 | * ReHEN [[paper](http://delivery.acm.org/10.1145/3360000/3351149/p1814-yang.pdf?ip=202.120.235.180&id=3351149&acc=OPEN&key=BF85BBA5741FDC6E%2E88014DC677A1F2C3%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1573634982_715c64cb335fa08b82d82225f1944231#URLTOKEN#)][[code](https://github.com/nnUyi/ReHEN)][[web](https://nnuyi.github.io/)] 128 | * Yang, Youzhao et al. Single Image Deraining via Recurrent Hierarchy and Enhancement Network. (2019 ACM'MM) 129 | 130 | * DTDN [[paper](http://delivery.acm.org/10.1145/3360000/3350945/p1833-wang.pdf?ip=202.120.235.223&id=3350945&acc=OPEN&key=BF85BBA5741FDC6E%2E88014DC677A1F2C3%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1572964912_ad2b0e3c2bc1fdb6f216a99468d1a0ea#URLTOKEN#)][code][web] 131 | * Wang, Zheng et al. DTDN: Dual-task De-raining Network. (2019 ACM'MM) 132 | 133 | * GraNet [[paper](http://delivery.acm.org/10.1145/3360000/3350883/p1795-yu.pdf?ip=202.120.235.223&id=3350883&acc=OPEN&key=BF85BBA5741FDC6E%2E88014DC677A1F2C3%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1572964981_badf5608c2c0c67afa35ba86f50fe968#URLTOKEN#)][code][web] 134 | * Yu, Weijiang et al. Gradual Network for Single Image De-raining. (2019 ACM'MM) 135 | 136 | * AMPE-Net [[paper](https://arxiv.org/pdf/1905.05404.pdf)][code][web] 137 | * Wang, Yinglong et al. An Effective Two-Branch Model-Based Deep Network for Single Image Deraining. (2019 Arxiv) 138 | 139 | * ReMAEN [[paper](https://github.com/nnUyi/ReMAEN/tree/master/paper)][[code](https://github.com/nnUyi/ReMAEN)][[web](https://nnuyi.github.io/)] 140 | * Yang, Youzhao el al. Single Image Deraining using a Recurrent Multi-scale Aggregation and Enhancement Network. (2019 ICME) 141 | 142 | * Rain Wiper [[paper](https://share.weiyun.com/5MXcnlX)][code][web] 143 | * Liang, Xiwen et al. Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining. (2019 PG) 144 | 145 | * Dual-ResNet [[paper](https://arxiv.org/pdf/1903.08817v1.pdf)][[code](https://github.com/liu-vis/DualResidualNetworks)][web] 146 | * Liu, Xing et al. Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration. (2019 CVPR) 147 | 148 | * Heavy Rain Image Restoration [[paper](http://export.arxiv.org/pdf/1904.05050)][[code](https://github.com/liruoteng/HeavyRainRemoval)][[dataset](https://drive.google.com/file/d/1rFpW_coyxidYLK8vrcfViJLDd-BcSn4B/view)][web] 149 | * Li, Ruoteng et al. Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning. (2019 CVPR) 150 | 151 | * SPANet [[paper](https://arxiv.org/pdf/1904.01538.pdf)][[code](https://github.com/stevewongv/SPANet)][[web](https://stevewongv.github.io/derain-project.html)][[dataset](https://stevewongv.github.io/derain-project.html)] 152 | * Wang, Tianyu et al. Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset. (2019 CVPR) 153 | 154 | * Comprehensive Benchmark Analysis [[paper](https://arxiv.org/pdf/1903.08558.pdf)][[code](https://github.com/lsy17096535/Single-Image-Deraining)][[dataset](https://github.com/lsy17096535/Single-Image-Deraining)] 155 | * Li, Siyuan et al. Single Image Deraining: A Comprehensive Benchmark Analysis. (2019 CVPR) 156 | 157 | * DAF-Net [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Hu_Depth-Attentional_Features_for_Single-Image_Rain_Removal_CVPR_2019_paper.pdf)][[code](https://github.com/xw-hu/DAF-Net)][[web](https://xw-hu.github.io/)] 158 | * Hu, Xiaowei et al. Depth-attentional Features for Single-image Rain Removal. (2019 CVPR) 159 | 160 | * Semi-supervised Transfer Learning [[paper](https://arxiv.org/pdf/1807.11078.pdf)][[code](https://github.com/wwzjer/Semi-supervised-IRR)][web] 161 | * Wei, Wei et al. Semi-supervised Transfer Learning for Image Rain Removal. (2019 CVPR) 162 | 163 | * PReNet [[paper](https://arxiv.org/pdf/1901.09221.pdf)][[code](https://github.com/csdwren/PReNet)][web] 164 | * Ren, Dongwei et al. Progressive Image Deraining Networks: A Better and Simpler Baseline. (2019 CVPR) 165 | 166 | * UMRL-using-Cycle-Spinning [[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yasarla_Uncertainty_Guided_Multi-Scale_Residual_Learning-Using_a_Cycle_Spinning_CNN_for_CVPR_2019_paper.pdf)][[code](https://github.com/rajeevyasarla/UMRL--using-Cycle-Spinning)][[web](https://github.com/rajeevyasarla)] 167 | * Rajeev Yasarla et al. Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining. (2019 CVPR) 168 | 169 | * RR-GAN [[paper](http://vijaychan.github.io/Publications/2019_derain.pdf)][code][web] 170 | * Zhu, Hongyuan et al. RR-GAN: Single Image Rain Removal Without Paired Information. (2019 AAAI) 171 | 172 | * LPNet [[paper](https://arxiv.org/abs/1805.06173)][[code](https://xueyangfu.github.io/projects/LPNet.html)][[web](https://xueyangfu.github.io/)] 173 | * Fu, Xueyang et al. Lightweight Pyramid Networks for Image Deraining. (2019 TNNLS) 174 | 175 | * Morphological Networks [[paper](https://arxiv.org/pdf/1901.02411.pdf)][code][web] 176 | * Mondal et al. Morphological Networks for Image De-raining. (2019 Arxiv) 177 | 178 | ### 2018 179 | 180 | * QS Priors [[paper](https://arxiv.org/pdf/1812.08348.pdf)][code][web] 181 | * Wang et al. Rain Removal By Image Quasi-Sparsity Priors. (2018 Arxiv) 182 | 183 | * Linear model [[paper](https://arxiv.org/pdf/1812.07870.pdf)][code][web] 184 | * Wang et al. Removing rain streaks by a linear model. (2018 Arxiv) 185 | 186 | * Kernel Guided CNN [[paper](https://arxiv.org/pdf/1808.08545.pdf)][code][web] 187 | * Deng et al. Rain Streak Removal for Single Image via Kernel Guided CNN. (2018 Arxiv) 188 | 189 | * Physics-Based GAM [[paper](https://arxiv.org/pdf/1808.00605.pdf)][[code](https://sites.google.com/site/jspanhomepage/physicsgan/)][web] 190 | * Pan, Jinshan et al. Physics-Based Generative Adversarial Models for Image Restoration and Beyond. (2018 Arxiv) 191 | 192 | * Self-supervised Constraints [[paper](https://arxiv.org/pdf/1811.08575.pdf)][code][paper] 193 | * Jin et al. Unsupervised Single Image Deraining with Self-supervised Constraints. (2018 Arxiv) 194 | 195 | * SRSE-Net [[paper](https://arxiv.org/pdf/1811.04761.pdf)][code][web] 196 | * Ye et al. Self-Refining Deep Symmetry Enhanced Network for Rain Removal. (2018 Arxiv) 197 | 198 | * Tree-Structured Fusion Model [[paper](https://arxiv.org/pdf/1811.08632.pdf)][code][web] 199 | * Fu, Xueyang et. al. A Deep Tree-Structured Fusion Model for Single Image Deraining. (2018 Arxiv) 200 | 201 | * Deep DCNet [[paper](https://arxiv.org/abs/1804.02688)][code] 202 | [[web1](https://sites.google.com/view/xjguo/homepage)] [[web2](https://sites.google.com/view/xjguo/homepage)] 203 | * Li, Siyuan et al. Fast Single Image Rain Removal via a Deep Decomposition-Composition Network. (ArXiv2018) 204 | 205 | * SFARL Model [[paper](https://arxiv.org/abs/1804.04522)][code][[web](https://sites.google.com/site/csrendw/home)] 206 | * Ren, Dongwei et al. Simultaneous Fidelity and Regularization Learning for Image Restoration. (ArXiv2018) 207 | 208 | * GCAN [[paper](https://arxiv.org/pdf/1811.08747.pdf)][[code](https://github.com/cddlyf/GCANet)][web] 209 | * Chen et. al. Gated Context Aggregation Network for Image Dehazing and Deraining. (2018 WACV) 210 | 211 | * Cycle-GAN [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8397790)][code][web] 212 | * Pu, Jinchuan et al. Removing rain based on a Cycle Generative Adversarial Network. (2018 ICIEA) 213 | 214 | * RESCAN [[paper](https://arxiv.org/pdf/1807.05698.pdf)][[code](https://xialipku.github.io/RESCAN/)][[web](https://xialipku.github.io/RESCAN/)] 215 | * Li, Xia et al. Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining. (2018 ECCV) 216 | 217 | * RGFFN [[paper](https://arxiv.org/abs/1804.07493)][code][web] 218 | * Fan, Zhiwen et al. Residual-Guide Feature Fusion Network for Single Image Deraining. (2018 ACM'MM) 219 | 220 | * NLEDN [[paper](https://arxiv.org/pdf/1808.01491.pdf)][[code](https://github.com/AlexHex7/NLEDN)][web] 221 | * Li, Guanbin et al. Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining. (2018 ACM'MM) 222 | 223 | * DualCNN [[paper](http://faculty.ucmerced.edu/mhyang/papers/cvpr2018_dual_cnn.pdf)][[code](https://sites.google.com/site/jspanhomepage/dualcnn)][[web](https://sites.google.com/site/jspanhomepage/dualcnn)] 224 | * Pan, Jinshan et al. Learning Dual Convolutional Neural Networks for Low-Level Vision. (2018 CVPR) 225 | 226 | * Attentive GAN [[paper](https://arxiv.org/abs/1711.10098)][[code](https://github.com/rui1996/DeRaindrop)][[web](https://rui1996.github.io/)][[project](https://rui1996.github.io/raindrop/raindrop_removal.html)] [[reimplement code](https://github.com/MaybeShewill-CV/attentive-gan-derainnet)] 227 | * Qian, Rui et al. Attentive Generative Adversarial Network for Raindrop Removal from a Single Image. (2018 CVPR) 228 | (*tips: this research focuses on reducing the effets form the adherent rain drops instead of rain streaks removal*) 229 | 230 | * DID-MDN [[paper](https://arxiv.org/abs/1802.07412)][[code](https://github.com/hezhangsprinter/DID-MDN)][[web](https://sites.google.com/site/hezhangsprinter/)] 231 | * Zhang, He et al. Density-aware Single Image De-raining using a Multi-stream Dense Network. (2018 CVPR) 232 | 233 | * Directional global sparse model [[paper](https://www.sciencedirect.com/science/article/pii/S0307904X18301069)] 234 | [[code](http://www.escience.cn/system/file?fileId=98760)][[web](http://www.escience.cn/people/dengliangjian/index.html)] 235 | * Deng, Liangjian et al. A directional global sparse model for single image rain removal. (2018 AMM) 236 | 237 | * Gradient domain [[paper](https://www.sciencedirect.com/science/article/pii/S0031320318300700)][code][web] 238 | * Du, Shuangli et al. Single image deraining via decorrelating the rain streaks and background scene in gradient domain. (2018 PR) 239 | 240 | ### 2017 241 | * ID_CGAN [[paper](https://arxiv.org/abs/1701.05957)][[code](https://github.com/hezhangsprinter/ID-CGAN)] [[web](http://www.rci.rutgers.edu/~vmp93/index_ImageDeRaining.html)] 242 | * Zhang, He et al. Image De-raining Using a Conditional Generative Adversarial Network. (2017 Arxiv) 243 | 244 | * Transformed Low-Rank Model [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Chang_Transformed_Low-Rank_Model_ICCV_2017_paper.html)][code][web] 245 | * Chang, Yi et al. Transformed Low-Rank Model for Line Pattern Noise Removal. (2017 ICCV) 246 | 247 | * JBO [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Zhu_Joint_Bi-Layer_Optimization_ICCV_2017_paper.html)][code][[web](http://appsrv.cse.cuhk.edu.hk/~lzhu/)] 248 | * Wei, Wei et al. Joint Bi-layer Optimization for Single-image Rain Streak Removal. (2017 ICCV) 249 | 250 | * JCAS [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Gu_Joint_Convolutional_Analysis_ICCV_2017_paper.html)][[code](http://www4.comp.polyu.edu.hk/~cslzhang/code/JCAS_Release.zip)][[web](https://sites.google.com/site/shuhanggu/home)] 251 | * Gu, Shuhang et al. Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation. (2017 ICCV) 252 | 253 | * DDN [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Removing_Rain_From_CVPR_2017_paper.pdf)] [[code](https://xueyangfu.github.io/projects/cvpr2017.html)][[web](https://xueyangfu.github.io/projects/cvpr2017.html)] 254 | * Fu, Xueyang et al. Removing rain from single images via a deep detail network. (2017 CVPR) 255 | 256 | * JORDER [[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yang_Deep_Joint_Rain_CVPR_2017_paper.pdf)] [[code](http://www.icst.pku.edu.cn/struct/Projects/joint_rain_removal.html)][[web](http://www.icst.pku.edu.cn/struct/people/whyang.html)] 257 | * Yang, Wenhan et al. Deep joint rain detection and removal from a single image. (2017 CVPR) 258 | 259 | * Hierarchical Approach [[paper](http://ieeexplore.ieee.org/abstract/document/7934435/)][code][web] 260 | * Wang, Yinglong et al. A Hierarchical Approach for Rain or Snow Removing in a Single Color Image. (2017 TIP) 261 | 262 | * Clearing The Skies [[paper](https://ieeexplore.ieee.org/abstract/document/7893758/)][[code](https://xueyangfu.github.io/projects/tip2017.html)][[web](https://xueyangfu.github.io/projects/tip2017.html)] 263 | * Fu, Xueyang et al. Clearing the skies: A deep network architecture for single-image rain removal. (2017 TIP) 264 | 265 | * Error-optimized Sparse Representation [[paper](https://ieeexplore.ieee.org/abstract/document/7878618/)][code][web] 266 | * Chen, Bohao et al. Error-optimized sparse representation for single image rain removal. (2017 TIE) 267 | 268 | ### 2015-2016 269 | * LP(GMM) (2016 CVPR, 2017 TIP) 270 | * Li, Yu et al. Rain streak removal using layer priors. [[paper](https://ieeexplore.ieee.org/document/7780668/)][code][web] 271 | * Li, Yu et al. Single Image Rain Streak Decomposition Using Layer Priors. [[paper](https://ieeexplore.ieee.org/abstract/document/7934436/)] 272 | [[dataset](http://yu-li.github.io/paper/li_cvpr16_rain.zip)][[web](http://yu-li.github.io/)] 273 | 274 | * DSC [[paper](http://ieeexplore.ieee.org/document/7410745/)][[code](http://www.math.nus.edu.sg/~matjh/download/image_deraining/rain_removal_v.1.1.zip)][web] 275 | * Luo, Yu et al. Removing rain from a single image via discriminative sparse coding. (2015 ICCV) 276 | 277 | * Window Covered [[paper](https://cs.nyu.edu/~deigen/rain/)][[code](https://cs.nyu.edu/~deigen/rain/)][web] 278 | * David, Eigen et al. Restoring An Image Taken Through a Window Covered with Dirt or Rain. (2013 ICCV) 279 | 280 | * Image Decomposition [paper](http://www.ee.nthu.edu.tw/cwlin/Rain_Removal/tip_rain_removal_2011.pdf)][[code](http://www.ee.nthu.edu.tw/~cwlin/pub.htm)][web] 281 | * Kang, Liwei et al. Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition. (2012 TIP) 282 | 283 | ## 4 Video Based Deraining 284 | ### 2019 285 | * D3R-Net [[paper](http://www.icst.pku.edu.cn/struct/Pub%20Files/2019/ywh_tip19.pdf)][code][web] 286 | * Yang, Wenhan et al. D3R-Net: Dynamic Routing Residue Recurrent Network for Video Rain Removal. (2019 TIP) 287 | 288 | ### 2018 289 | * MSCSC [[paper](https://pan.baidu.com/s/1iiRr7ns8rD7sFmvRFcxcvw)][[code](https://github.com/MinghanLi/MS-CSC-Rain-Streak-Removal)] [[web](https://sites.google.com/view/cvpr-anonymity)][[video](https://www.youtube.com/watch?v=tYHX7q0yK4M)] 290 | * Li, Minghan et al. Video Rain Streak Removal By Multiscale ConvolutionalSparse Coding. (2018 CVPR) 291 | 292 | * CNN Framework [[paper](https://arxiv.org/abs/1803.10433)][code][[web Chen](https://github.com/hotndy/SPAC-SupplementaryMaterials)] [[web Chau](http://www.ntu.edu.sg/home/elpchau/)] 293 | * Chen, Jie et al. Robust Video Content Alignment and Compensation for Rain Removal in a CNN Framework. (2018 CVPR) 294 | * Chen, Jie et al. Robust Video Content Alignment and Compensation for Clear Vision Through the Rain [[paper](https://arxiv.org/abs/1804.09555)][code][web](*tips: I guess this is the extended journal version*) 295 | 296 | * Erase or Fill [[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Erase_or_Fill_CVPR_2018_paper.pdf)][[code](https://github.com/flyywh/J4RNet-Deep-Video-Deraining-CVPR-2018)][[web Liu](http://www.icst.pku.edu.cn/struct/people/liujiaying.html)] [[web Yang](http://www.icst.pku.edu.cn/struct/people/whyang.html)] 297 | * Liu, Jiaying et al. Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos. (2018 CVPR) 298 | 299 | ### 2017 300 | * MoG [[paper](http://openaccess.thecvf.com/content_iccv_2017/html/Wei_Should_We_Encode_ICCV_2017_paper.html)] 301 | [[code](https://github.com/wwxjtu/RainRemoval_ICCV2017)][[web](https://github.com/wwxjtu/RainRemoval_ICCV2017)] 302 | * Wei, Wei et al. Should We Encode Rain Streaks in Video as Deterministic or Stochastic? (2017 ICCV) 303 | 304 | * FastDeRain [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Jiang_A_Novel_Tensor-Based_CVPR_2017_paper.html)][[code](https://github.com/TaiXiangJiang/FastDeRain)] 305 | * Jiang, Taixiang et al. A novel tensor-based video rain streaks removal approach via utilizing discriminatively intrinsic priors. (2017 CVPR) 306 | 307 | * Matrix Decomposition [[paper](http://openaccess.thecvf.com/content_cvpr_2017/html/Ren_Video_Desnowing_and_CVPR_2017_paper.html)][code][web] 308 | * Ren, Weilong et al. Video Desnowing and Deraining Based on Matrix Decomposition. (2017 CVPR) 309 | 310 | ### 2015-2016 311 | * Adherent Raindrop Modeling [[paper](https://ieeexplore.ieee.org/abstract/document/7299675/)][code][[web](http://www.cvl.iis.u-tokyo.ac.jp/~yousd/CVPR2013/Shaodi_CVPR2013.html)] 312 | * You, Shaodi et al. Adherent raindrop modeling, detectionand removal in video. (2016 TPAMI) 313 | 314 | * Low-rank Matrix Completion [[paper](https://ieeexplore.ieee.org/abstract/document/7101234/)][[code](http://mcl.korea.ac.kr/~jhkim/deraining/)][web] 315 | * Kim, JH et al. Video deraining and desnowing using temporal correlation and low-rank matrix completion. (2015 TIP) 316 | 317 | * Utilizing Local Phase Information [[paper](https://link.springer.com/article/10.1007/s11263-014-0759-8)][code][web] 318 | * Santhaseelan et al. Utilizing local phase information to remove rain from video. (2015 IJCV) 319 | 320 | ## 5 Acknowledgement 321 | - Thanks for the sharing of codes of image quality metrics by [Wang, Hong](https://github.com/hongwang01/Video-and-Single-Image-Deraining). 322 | 323 | ## 6 Contact 324 | - e-mail: yzyang17@fudan.edu.cn 325 | --------------------------------------------------------------------------------