[1]WANG Keping,CAI Kaili,WANG Hongqi,et al.A global sparse rain removal model based on rain streaks main direction adaptation[J].CAAI Transactions on Intelligent Systems,2020,15(2):271-280.[doi:10.11992/tis.201809042]
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A global sparse rain removal model based on rain streaks main direction adaptation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 2 Page number: 271-280 Column: 学术论文—智能系统 Public date: 2020-03-05
Title:
A global sparse rain removal model based on rain streaks main direction adaptation
Author(s):
WANG Keping;  CAI Kaili;  WANG Hongqi;  YANG Yi
College of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454150, China
Keywords:
single-image rain removal; main directional of the rain streaks; the image block; HOG feature; global sparse model; sparse regularization term; color mask; reorganization by the channel image
CLC:
TP391.4
DOI:
10.11992/tis.201809042
Abstract:
The existing single-image rain removal algorithms do not appropriately consider the influence of wind on the main direction of the rain streaks. When the rain streak deviates from the vertical direction, the existing methods do not take rotation or only rotate roughly, resulting in the phenomenon whereby rain streaks are residual or the background is blurred. Therefore, in this paper, we propose a global sparse rain removal model based on the rain streaks main direction adaptation. First, the image block with the smallest variance and the rain streaks image of the image library are matched according to the histogram of oriented gradients (HOG) feature, and the main direction of the rain streaks image with the highest matching degree is regarded as the main direction of the image block, which can determine the rotation angle of the global sparse model; then, the global sparse model with three sparse regular terms including rotation angles is used for rain removal. After removing the rain streaks from the global sparse model, the Y-channel image is enhanced by a color mask, and thus, some parts of the background are protected. Then, together with the original CbCr-channel images, the image after treatment is further reorganized, and the final image after rain removal is obtained. The results show that compared with three typical comparison algorithms, the peak signal-to-noise ratio and the structural similarity are improved, and the running time is shorter. The proposed method can retain the background details of the image as much as possible while effectively removing the rain streaks.
References:
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