Open AccessRain-removing Algorithm for Image Based on Deep Learning
Author(s) -
Qingping Zou,
Jing Wang,
Shiting Luo
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1533/3/032050
Subject(s) - subnetwork , rainwater harvesting , computer science , deep learning , image (mathematics) , artificial intelligence , residual , layer (electronics) , algorithm , pattern recognition (psychology) , environmental science , computer network , ecology , chemistry , organic chemistry , biology
Aiming at the problem that the decrease of driver’s visual definition is affected by rainy weather, a new deep learning image rain removal algorithm is proposed. The network consists of a rainwater detection subnetwork and a rainwater removal subnetwork in series. The rainwater detection subnetwork adopts residual learning network, which can accurately learn the difference between rain images and non-rain images. The rain-removing subnetwork uses a U-shaped network with dense connections. On the one hand, it uses the U-shaped network to retain the details of the background, and on the other hand, the DenseNet is used to multiplex the lower layer features to higher layer features to improve the accuracy of rain removal, by combining them, it alleviates the contradiction between the loss of background details caused by excessive rain removal and the incomplete rain-removing. Experimental results show that this kind of deep rain-removing network can detect and remove the rainwater in the image well.