Deep Unfolding Network for Image Desnowing With Snow Shape Prior

Effectively leveraging snow image formulation, which accounts for atmospheric light and snow masks, is crucial for enhancing image desnowing performance and improving interpretability. However, current direct-learning approaches often neglect this formulation, while model-based methods use it in overly simplistic ways. To address this, we propose a novel unfolding network that iteratively refines the desnowing process for more thorough optimization. Additionally, model-based techniques usually rely on real-world snow masks for supervision, a requirement that is impractical in many real-world applications. To overcome this limitation, we introduce a snow shape prior as a surrogate supervision signal. We further integrate the physical properties of atmospheric light and heavy snow by decomposing the optimization task into manageable sub-problems within our unfolding network. Extensive evaluations on multiple benchmark datasets confirm that our method outperforms current state-of-the-art techniques.