基于注意力生成对抗网络的单幅图像去雨方法

下雨是一种常见的天气现象,而滞留在图像上的雨条纹降低了图像的清晰度以及影响了基于该图像的后续图像处理.从图像中去除雨的关键是如何准确、鲁棒地识别图像中的雨区域.使用导向滤波器和Haar小波变换组成的雨线提取模块来增强雨条纹特征提取,然后通过空间关注模块生成雨线注意力图,以准确定位雨条纹的位置.两者结合后,获得降雨条纹的...

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

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.