GBPG-Net: Global Background Prior-Guided Rain and Snow Image Restoration

Xiao Liu, Xiaofeng Wang, Shouyi Wang, Haosong Gou, Zhengyong Wang, Chao Ren

Abstract: The aim of image restoration in the presence of rain and snow effects is to eliminate these disturbances while retaining the underlying background structure. Most existing methods tend to directly learn the mapping from corrupted images to clean ones, often resulting in residual rain or snow artifacts and compromised background structures. In this work, both theoretical analysis and experimental findings confirm the robustness of the hue channel in HSV color space to rain and snow disturbances, even when extracted from corrupted images. Motivated by this insight, we propose to leverage the global clean background cues inherent in the hue channel to guide the network in preserving the image background structure and removing interference. To this end, we introduce the global background prior-guided network (GBPG-Net) for restoring rain and snow-affected images, which employs a triangular formation to facilitate continuous interaction and updating of the global background prior (GBP) with the image feature within the GBPG-unit, resulting in improved interference removal and background structure preservation. Specifically, the GBPG-Net incorporates the global clean background prior injector (GCBPI) to inject the GBP into the network. Subsequently, the prior-guided local detail excavation (PGLDE) module, built on GCBPI, further refines interference removal and structure preservation to process local details intricately. Finally, the prior-guided local-global aggregation (PGLGA) module aggregates global background features with local detailed features, enabling the network to better understand the overall content and subtle interference for more accurate reconstruction. Quantitative and qualitative evaluations on synthetic and real datasets demonstrate the effectiveness of the proposed GBPG-Net in deraining and desnowing tasks, highlighting its advantages over existing methods. The code and supplementary documentation are available at https://github.com/liux520/GBPG-Net

External IDs:dblp:journals/tnn/LiuWWGWR25