Context-aware coarse-to-fine network for single image desnowing

Image desnowing is a challenging task in computer vision, as it requires the removal of snow from images while preserving the underlying scene structure and content. In order to achieve high performance, desnowing methods need to be able to effectively capture both local and global information in the image. The proposed method addresses this challenge by introducing a novel Context-aware Feature Aggregation (CFA) module. The CFA module is designed to capture both local and global information by aggregating features of the network in latent space. This allows the method to better understand the contextual relationships in the image, which is essential for accurate snow removal. In addition to the CFA module, the proposed method also introduces a Selective Refinement Head (SRH). The SRH is designed to adaptively fuse coarse features from the encoder and decoder of the network. This allows the method to refine the output by incorporating relevant information from both low-level and high-level representations. Finally, the proposed method leverages the capabilities of contrastive learning to better align the desnow images and ground-truth images in perceptual space. This leads to improved image quality and desnowing performance. Extensive experiments on both synthetic and real-world datasets show that the proposed method achieves state-of-the-art results on image desnowing task.