Single Image Rain Removal Method Based on Progressive Multi-scale Attention Residual Network
Rains can exert severely impacts on the visibility of scenes,reducing the quality of imaging and affecting a large number of computer vision tasks and systems,such as video surveillance and self-driving car and the like.Eliminating rain streaks,therefore,is a crucial task.This paper proposes a novel deraining model,coined as progressive multi-scale attention residual net-work(PMARnet),to remove rain streaks from a single frame image.Considering that complex scenes usually consist of multiple rain layers,PMARnet contains several stages.Each of them possess a residual network to alleviate gradient vanishing.In further,a multi-scale fusion attention residual model(MAR)is proposed to better characterize the semantic feature and local spatial feature in detail for each rain layer.Two publicly available benchmark datasets,Rain100H and Rain100L,are used for experimental valida-tion.Compared with eleven existing advanced methods,PMARnet performs the best with an PSNR of 28.06 and an SSIM of 0.89 for Rain100H and accordingly 37.25 and 0.98 for Rain100L.Compared with the second best method,there is an improvement of 2.41%and 1.14%for Rain100H and that of 3.16%and 1.03%for Rain100L.In this study,the proposed PMARnet can effectively propa-gate information between the rain streaks layer and the clean background image layer.PMARnet makes good use of rain streaks layer and background layer,and can achieve good rain removal effect.
single image rain removaldeep learningprogressive image derainingmulti-scale fusionattention network
国家科技期刊平台
NSTL
万方数据
