Optimized connections and feature interactions for more efficient single-image desnowing

The challenge of single image desnowing primarily stems from the diversity and irregular shape of snow. While existing methods can effectively remove snow particles of various shapes, they often introduce distortion to the restored images. To address the challenges posed by the diverse shapes and sizes of snow particles, as well as the issue of distortion after desnowing, we propose a novel single image desnowing network called Star-Net. Our approach designs a Star type Skip Connection (SSC), which establishes information channels for different scale features. This design allows the network to aggregate all scale features, making it easier to handle snow particles with complex shapes and varying sizes. Additionally, we design a Multi-Stage Interactive Transformer (MIT) as the foundational module of Star-Net to solve image distortion. MIT explicitly models a range of essential image recovery features (e.g., local features, multi-scale features) by combining the advantages of convolution and attention mechanisms to restore regions of image distortion and further enhance the comprehension of different snow particle shapes and sizes. Furthermore, through experimental observations, we identify the presence of snow particle residuals within the SSC. To address this, we propose a Degenerate Filter Module (DFM) that filters out snow particle residuals in the SSC across spatial and channel domains. Extensive experiments on standard snow removal datasets and real-world datasets demonstrate that Star-Net achieves state-of-the-art performance on snow removal tasks. Importantly, our approach retains the original sharpness of the images while effectively removing snow.