Learning Dual-Domain Multi-Scale Representations for Single Image Deraining

2025-03-15

AI Summary

Existing single-image deraining methods are largely confined to single-domain (spatial-only) and single-scale modeling, limiting their ability to jointly exploit multi-scale external and internal features and inadequately capturing complex real-world rain streaks. To address these limitations, we propose a dual-domain collaborative multi-scale representation framework. Our approach introduces, for the first time, a parallel multi-scale representation mechanism operating simultaneously in both spatial and frequency domains. We design a hierarchical modulation and fusion module (MPSRM) and a frequency-domain scale-mixing module (FDSM) to enable cross-domain feature coupling and progressive spatial refinement. By integrating Fourier-based frequency-domain modeling, multi-scale feature pyramids, and deep learning, our method effectively balances local detail preservation and global structural dependencies. Extensive experiments demonstrate state-of-the-art performance across six benchmark datasets, with significant improvements in restoration accuracy and structural fidelity—particularly under challenging, realistic rain conditions.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Overcomes limitations of single-scale architectures in image deraining
• Addresses restrictive single-domain representations in real-world rain scenarios
• Proposes dual-domain multi-scale network for comprehensive rain removal

Innovation

• Dual-Domain Multi-Scale Representation Network (DMSR)
• Multi-Scale Progressive Spatial Refinement Module (MPSRM)
• Frequency Domain Scale Mixer (FDSM)