DDCNet: Advanced Decoupling of Degradation and Content for Adverse Weather Image Restoration

Adverse weather image restoration aims to recover clear images from those affected by weather conditions such as rain, haze, and snow. Different weather types affect images in distinct ways, necessitating specific degradation removal strategies, while content reconstruction generally benefits from a consistent approach since the underlying image structure remains largely consistent. Previous methods, despite their ability to handle multiple weather conditions within a single framework, often failed to adequately separate these two critical processes, thereby adversely affecting image restoration quality. In this article, we present DDCNet, a novel framework designed to explicitly decouple degradation removal and content reconstruction when processing various adverse weather conditions within a unified network. We achieve this by separating tailored degradation removal from uniform content reconstruction at the feature level, based on channel statistics. Additionally, we utilize the Fourier transform to enhance both processes. Furthermore, to address the differing optimization directions required by different adverse weather types, we propose a novel degradation mapping (DM) loss function to constrain their respective optimization paths. Extensive experiments show that DDCNet establishes new performance standards across multiple adverse weather scenarios.