Multi-Resolution Context Aggregation Network for Single Image Rain Removal

Abstract

Severe weather such as rain will cause serious degradation of image quality and affect the accuracy of computer vision algorithms. In order to better extract the features of multi-scale rain streaks and restore the important detailed feature information of the image, a new single image rain removal based on multi-resolution context aggregation network is proposed. Firstly, the shuffling operation is used to convert a single resolution input image into a multi-spatial resolution input, which enables the network to rapidly expand the acceptance field at the low spatial resolution and extract more refined rain streak features at high spatial resolution. The low-resolution rain streak features are aggregated from top to bottom into high spatial resolution, and the network is guided to extract multi-scale rain streak information. Secondly, the multi-resolution feature enhancement block is used to refine the details of the image at different resolutions to prevent the loss or blurring of the details in the rain-removed image. This method uses local residual densely connect blocks and squeeze-excitation network to enhance the ability and efficiency of the network to extract features. Finally, the mixed loss function constructed in this paper is used to obtain higher evaluation index values while improving the human visual perception of rain removal images. The experimental results show that proposed method achieves significant rain removal effects on the Rain100H, Rain100L, and Rain12 synthetic datasets and real rain datasets. Compared with the existing algorithms, the proposed method can achieve significant improvement in qualitative and quantitative indicators and has a high degree of detail retention.