P‐4.11: Image Deraining using Multi‐scale Generative Adversarial Network

The images obtained for automatic driving and security monitoring are easily affected by rain, which will bring serious difficulties to subsequent image‐processing tasks. To tackle the problem, deraining algorithms are widely adopted. However, most of the traditional methods fail to restore rain ima...

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Bibliographic Details
Published in:SID International Symposium Digest of technical papers 2023-04, Vol.54 (S1), p.664-668
Main Authors: Lin, Qinyi, Yan, Limin
Format: Article
Language:English
Subjects:
Algorithms
deraining
Generative Adversarial Network(GAN)
Generative adversarial networks
Image restoration
Multi-scale
Rain
Raindrops
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Summary:The images obtained for automatic driving and security monitoring are easily affected by rain, which will bring serious difficulties to subsequent image‐processing tasks. To tackle the problem, deraining algorithms are widely adopted. However, most of the traditional methods fail to restore rain images when raindrops come in different sizes and densities. In this paper, a single‐image deraining algorithm based on multi‐scale generative adversarial network (MS‐GAN) is proposed. This algorithm uses three‐level Laplace pyramid to decompose the input rain image into different scales, inputs them into three modified residual networks and uses adversarial network to improve the effect of learning. Experimental results show that the proposed algorithm can remove raindrops more efficiently and reduce loss of detail, even when raindrops is of different sizes.
ISSN:0097-966X
2168-0159
DOI:10.1002/sdtp.16379