Learning Dual-Domain Multi-Scale Representations for Single Image Deraining
This addresses the challenge of handling complex real-world rain scenarios in computer vision, representing an incremental improvement over existing methods.
The paper tackles the problem of single image deraining by proposing a Dual-Domain Multi-Scale Representation Network (DMSR) that exploits joint multi-scale representations from external and internal domains in spatial and frequency domains, achieving state-of-the-art performance across six benchmark datasets.
Existing image deraining methods typically rely on single-input, single-output, and single-scale architectures, which overlook the joint multi-scale information between external and internal features. Furthermore, single-domain representations are often too restrictive, limiting their ability to handle the complexities of real-world rain scenarios. To address these challenges, we propose a novel Dual-Domain Multi-Scale Representation Network (DMSR). The key idea is to exploit joint multi-scale representations from both external and internal domains in parallel while leveraging the strengths of both spatial and frequency domains to capture more comprehensive properties. Specifically, our method consists of two main components: the Multi-Scale Progressive Spatial Refinement Module (MPSRM) and the Frequency Domain Scale Mixer (FDSM). The MPSRM enables the interaction and coupling of multi-scale expert information within the internal domain using a hierarchical modulation and fusion strategy. The FDSM extracts multi-scale local information in the spatial domain, while also modeling global dependencies in the frequency domain. Extensive experiments show that our model achieves state-of-the-art performance across six benchmark datasets.