Uncertainty-Driven Dynamic Degradation Perceiving and Background Modeling for Efficient Single Image Desnowing

Single-image snow removal aims to restore clean images from heterogeneous and irregular snow degradations. Recent methods utilize neural networks to remove various degradations directly. However, these approaches suffer from the limited ability to flexibly perceive complicated snow degradation patterns and insufficient representation of background structure information. To further improve the performance and generalization ability of snow removal, this paper aims to develop a novel and efficient paradigm from the perspective of degradation perceiving and background modeling. For this purpose, we first analyze two critical properties in real snow images, namely local-region heterogeneity and axial anisotropy. Inspired by them, we propose Dynamic Perceiving for Degraded Regions and Axial-Pooling Attention for Background Structure Modeling, which together couple a new network architecture, dubbed as D2P-BMNet. Our proposed D2P-BMNet offers several key advantages: (i) It can effectively segment regions under the uncertainty map's guidance, and dynamically perceives heterogeneous degradations within various regions. (ii) By utilizing linear attention solely along a horizontal axis, it can effectively model clean scene information that is buried beneath the snow. (iii) D2P-BMNet significantly improves over prior methods across all benchmarks and maintains excellent inference speeds.