Dual-level Prototype Learning for Composite Degraded Image Restoration

Images captured under severe weather conditions often suffer from complex, composite degradations, varying in intensity. In this paper, we introduce a novel method, Dual-Level Prototype Learning (DPL), to tackle the challenging task of composite degraded image restoration. Unlike previous methods that rely on fixed embeddings to characterize degradation types, DPL maintains a number of degradation-level prototypes to represent the specific degradation scenes dynamically. Furthermore, considering the diverse factors influencing each degradation type, factor-level prototypes are incorporated to capture variations in individual degradation factors. Image features are matched with both degradation-level and factor-level prototypes, producing detailed scene embeddings that enhance the network's understanding of composite degradations. These scene embeddings are then processed through Dual Scene Embedding Transformer Blocks to guide the restoration process. To further refine the prototype distribution, we propose a Prototype Scatter Learning Loss, which enables prototypes within the same degradation to learn more information and push prototypes between different degradations to be separate. Additionally, we introduce a new dataset named Variable Composite Degradation (VCD) dataset which contains images with different intensities of each type of composite degradation to validate the efficacy of our method. Extensive experiments demonstrate that DPL significantly outperforms existing methods in restoring images with composite degradations.