Survey on rain removal from videos or a single image  

作　　者：Hong WANG Yichen WU Minghan LI Qian ZHAO Deyu MENG 

机构地区：[1]School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security,Xi'an Jiaotong University,Xi'an 710049,China [2]Pazhou Lab,Guangzhou 510330,China

出　　处：《Science China(Information Sciences)》2022年第1期68-90,共23页中国科学（信息科学）（英文版）

基　　金：supported by the National Key R&D Program of China(Grant No.2020YFA0713900);National Natural Science Foundation of China(Grant Nos.11690011,61721002,U1811461)。

摘　　要：Rain can cause performance degradation of outdoor computer vision tasks.Thus,the exploration of rain removal from videos or a single image has drawn considerable attention in the field of image processing.Recently,various deraining methodologies have been proposed.However,no comprehensive survey work has yet been conducted to summarize existing deraining algorithms and quantitatively compare their generalization ability,and especially,no off-the-shelf toolkit exists for accumulating and categorizing recent representative methods for easy performance reproduction and deraining capability evaluation.In this regard,herein,we present a comprehensive overview of existing video and single image deraining methods as well as reproduce and evaluate current state-of-the-art deraining methods.In particular,these approaches are mainly classified into model-and deep-learning-based methods,and more elaborate branches of each method are presented.Inherent abilities,especially generalization performance,of the state-of-the-art methods have been both quantitatively and visually analyzed through thorough experiments conducted on synthetic and real benchmark datasets.Moreover,to facilitate the reproduction of existing deraining methods for general users,we present a comprehensive repository with detailed classification,including direct links to 85 deraining papers,24 relevant project pages,source codes of 12 and 25 algorithms for video and single image deraining,respectively,5 and 10 real and synthesized datasets,respectively,and 7 frequently used image quality evaluation metrics,along with the corresponding computation codes.Research limitations worthy of further exploration have also been discussed for future research along this direction.

关 键 词：rain removal maximum a posterior estimation deep learning generalization performance comprehensive repository 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]