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Open AccessArticle

A Shearlets-Based Method for Rain Removal from Single Images

1
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
2
Engineering School, DEIM, University of Tuscia, 01100 Viterbo, Italy
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(23), 5137; https://doi.org/10.3390/app9235137
Received: 21 October 2019 / Revised: 14 November 2019 / Accepted: 21 November 2019 / Published: 27 November 2019
This work focuses on the problem of rain removal from a single image. The directional multilevel system, Shearlets, is used to describe the intrinsic directional and structure sparse priors of rain streaks and the background layer. In this paper, a Shearlets-based convex rain removal model is proposed, which involves three sparse regularizers: including the sparse regularizer of rain streaks and two sparse regularizers of the Shearlets transform of background layer in the rain drops’ direction and the Shearlets transform of rain streaks in the perpendicular direction. The split Bregman algorithm is utilized to solve the proposed convex optimization model, which ensures the global optimal solution. Comparison tests with three state-of-the-art methods are implemented on synthetic and real rainy images, which suggests that the proposed method is efficient both in rain removal and details preservation of the background layer. View Full-Text
Keywords: rain removal; shearlets; split Bregman algorithm rain removal; shearlets; split Bregman algorithm
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MDPI and ACS Style

Sun, G.; Leng, J.; Cattani, C. A Shearlets-Based Method for Rain Removal from Single Images. Appl. Sci. 2019, 9, 5137. https://doi.org/10.3390/app9235137

AMA Style

Sun G, Leng J, Cattani C. A Shearlets-Based Method for Rain Removal from Single Images. Applied Sciences. 2019; 9(23):5137. https://doi.org/10.3390/app9235137

Chicago/Turabian Style

Sun, Guomin; Leng, Jinsong; Cattani, Carlo. 2019. "A Shearlets-Based Method for Rain Removal from Single Images" Appl. Sci. 9, no. 23: 5137. https://doi.org/10.3390/app9235137

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