Abstract: Existing image deraining networks predominantly rely on the large-scale synthetic paired datasets for training,ignoring the difference in spatial distribution characteristics and the difference in channel importance between synthetic and real data,resulting in blurred texture details and diminished generalization performance.To address these issues,this paper develops an unsupervised network model for image deraining based on a union attention mechanism with multi-stage feature extraction.To adapt to the spatial locality of rain streaks,the feature-aware module is initially designed to extract rain streaks through the combination of spatial and channel attention mechanisms,while dilation convolution is used to enhance the sensory field of rain feature extraction.In addition,a recurrent neural network is introduced to extract the rain stripe features gradually,and the useful information of the previous stage is retained in the cycle to improve the rain stripe feature extraction ability.To further enhance the discrimination of local micro-details and global texture structure features,it designs a multi-scale discriminator for distinguishing images at three different scales and guideings the generator to produce higher quality images.Qualitative and quantitative experiments on synthetic and real datasets show that the proposed method is superior to some supervised,semi-supervised and unsupervised me-thods on PSNR,SSIM and NIQE metrics,which verifies its effectiveness and generalization.

Key words: Image processing, Image deraining, Multi-attention mechanism, Multi-stage feature extraction, Unsupervised learning