Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 10, 1409(2023)

Image rain removal algorithm based on multi-cascade progressive convolution structure

Yong ZHANG1,2, Jie-long GUO2、*, Fan WANG1,2, Hai LAN2, Hui YU2, and Xian WEI2
Author Affiliations
  • 1College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China
  • 2Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences,Fuzhou 350108,China
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    Rainy images can affect the performance and accuracy of computer vision tasks. Rainy images often contain raindrops or rain marks from different directions, sizes, and shapes. When removing these raindrops and rain marks, the existing methods often do not take into account the feature information of rainy images at different fine scales, and only use a single scale. There is a big defect in image deraining, and it is impossible to restore a clear enough image for visual tasks. Therefore, benefiting from the powerful feature extraction capability of the convolutional neural network architecture, an end-to-end multi-cascade progressive convolution structure operator is proposed, which consists of four convolutional layers connected through a ladder to form an overall module. This module can extract and integrate rainy weather features in multi-scale scenes. The operator module is embedded into the progressive recurrent network structure, the recurrent structure is used to remove rain streaks many times, and finally the rain-free image close to the real image is effectively restored. The method is compared with the existing artificially synthesized rain image datasets Rain100H, Rain100L, Rain800 and the synthetic rain image dataset BDD1000 in the field of automatic driving. The experiment results shows that the PSNR values of the algorithm on the four datasets reach 30.70 , 37.91, 27.63, 35.74 dB, and the SSIM values reach 0.914, 0.980, 0.894, 0.977. Through the visual display of the rain removal results of the real rain map dataset, the effectiveness of the method in this paper on the rain removal task is fully verified.

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    Yong ZHANG, Jie-long GUO, Fan WANG, Hai LAN, Hui YU, Xian WEI. Image rain removal algorithm based on multi-cascade progressive convolution structure[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(10): 1409

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    Paper Information

    Category: Research Articles

    Received: Nov. 16, 2022

    Accepted: --

    Published Online: Oct. 25, 2023

    The Author Email: Jie-long GUO (gjl@fjirsm.ac.cn)

    DOI:10.37188/CJLCD.2022-0383

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