Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (15): 215-222.DOI: 10.3778/j.issn.1002-8331.2005-0178

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Single Image De-raining Method Based on Attention Generation Adversarial Network

ZHU Deli, XIONG Chang, HU Xuekui, LI Wei, WANG Qing   

  1. 1.College of Computer and Information Science, Chongqing Normal University, Chongqing 401331, China
    2.Chongqing Digital Agricultural Service Engineering Technology Research Center, Chongqing 401331, China
  • Online:2021-08-01 Published:2021-07-26



  1. 1.重庆师范大学 计算机与信息科学学院,重庆 401331
    2.重庆市数字农业服务工程技术研究中心,重庆 401331


Raining is a common weather phenomenon, and the rain streaks left on the image reduce the clarity of the image and affect the subsequent image processing based on the image. The key to removing rain from an image is how to accurately and robustly identify rain areas in the image. In this paper, the rain line extraction module composed of guided filter and Haar wavelet transform is used to enhance the rain stripe feature extraction, and then the rain line attention map is generated by the spatial attention module to accurately locate the position of the rain stripe. After combining the two, the foreground information of the rain stripes can be obtained and then generating the characteristics of the mutual game in the training mechanism of the adversarial network can enhance the position recognition ability of the rain bars and effectively remove the rain stripes. Experiments are conducted on the comprehensive test data set and real images. Compared with several deep network de-raining methods, the Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity ratio(SSIM) are improved. Experiments show that the network in this paper has excellent performance, and has a high generalization ability for different rain streak densities. At the same time, it can better maintain the original information of the image and avoid image blur.

Key words: generative adversarial networks, attention mechanism, image derain



关键词: 生成对抗网络, 注意力机制, 图像去雨