DEMNet: A degradation difference enabled multi-stage network for multiple degradation image restoration

Abstract

Restoring images affected by multiple types of degradation is a challenging and active area, requiring simultaneous handling of various forms of corruption and maintaining semantic context and spatial details while effectively managing network complexity. To advance the performance of multiple degradation image restoration tasks, we introduce a Degradation Difference Enabled Multi-stage Network (DEMNet), which can efficiently restore images affected by various types of degradation, including noise, rain, and haze. Three key contributions are provided by the proposed method: First, we introduce a unique three-stage architecture in DEMNet to balance capturing fine spatial details and the preservation of crucial contextual information. Second, we present a degradation difference enabled contrastive loss as a guidance mechanism for a degradation difference encoder. This loss function facilitates the accurate extraction of degradation-specific information, thus enhancing restoration. Finally, to balance channel features and spatial details, we propose a spatial-channel integration block, which improves the overall representation of the restored image with improved comprehensiveness and accuracy. By integrating these innovative modules, our DEMNet performs favorably against the latest approaches in multiple degradation image restoration while significantly reducing the number of parameters. Furthermore, DEMNet also exhibits excellent performance in single degradation image restoration, showcasing its versatility and effectiveness across various degradation types. Extensive experimental evaluations confirm the superiority of the proposed method.