Exploring Local Sparse Structure Prior for Image Deraining and Desnowing

Abstract

Existing image deraining and desnowing methods are typically trained under specific weather conditions, which limits their effectiveness in locating rain streaks and snowflakes in diverse, open scenes. This restriction often leads to suboptimal restoration performance. To address these limitations, we propose a novel local sparse structure prior for rain and snow, characterized by high pixel intensity and the locally sparse spatial distribution of rain streaks and snowflakes. Leveraging this prior, we developed an algorithm that extracts rain and snow structure masks, enabling precise localization of rain streaks and snowflake regions across open scenes. In addition, we introduce a refinement and compensation process to remove irrelevant information from the masks and correct mask estimation errors. We further construct a Mask-Guided Restoration Network (MGNet) that utilizes the rain and snow structure masks effectively and includes a mask-conditioned attention module to focus restoration efforts on degraded areas affected by rain streaks and snowflakes. Extensive experimental results demonstrate that our method significantly outperforms current state-of-the-art techniques in open scenes, effectively restoring various types of rain streaks and snowflakes with a single model parameter configuration.