Learning Dual-Domain Multi-Scale Representations for Single Image Deraining
Journal:
arXiv
Published Date:
Mar 15, 2025
Abstract
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.
