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SynFSNet:频域与空间域协同建模的图像去雨网络

李亚敏 向稳 刘钰婷 向尧

计算机工程2026,Vol.52Issue(8):58-70,13.
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计算机工程2026,Vol.52Issue(8):58-70,13.DOI:10.19678/j.issn.1000-3428.0253484

SynFSNet:频域与空间域协同建模的图像去雨网络

SynFSNet:Synergizing Frequency Domain and Spatial Domain Modeling Network for Image Deraining

李亚敏 1向稳 1刘钰婷 1向尧2

作者信息

  • 1. 湖北大学计算机学院,湖北武汉 430062||智能感知系统与安全教育部重点实验室,湖北武汉 430062||大数据智能分析与行业应用湖北省重点实验室(湖北大学),湖北武汉 430062
  • 2. 湖北大学计算机学院,湖北武汉 430062||武汉理工大学计算机与人工智能学院,湖北武汉 430070
  • 折叠

摘要

Abstract

Image deraining aims to restore high-quality clean images from rain-degraded inputs and is a key technique for improving the robustness of outdoor vision systems.Existing Convolutional Neural Network(CNN)-based methods are limited by local receptive fields and thus struggle to effectively model long-range dependencies.Although some Transformer-based methods enhance global modeling capability,they show limited ability in handling multi-scale and directional rain streaks as well as recovering local high-frequency details.Moreover,existing frequency-domain enhancement methods often treat the frequency domain merely as an auxiliary representation,which lack fine-grained collaborative modeling of the complementary relationship between the spatial and frequency domains.To address these issues,this study proposes a Synergizing Frequency and Spatial Network(SynFSNet)for image deraining.Unlike existing methods that primarily use the frequency domain as a global enhancement tool,the proposed method uses the directional,density-related,and scale-varying characteristics of rain streaks in the frequency domain.Specifically,a Multi-Scale Fourier Fusion Module(MSFFM)is designed to progressively suppress rain streaks in a coarse-to-fine manner.Additionally,a Fourier Shaped Attention Module(FSAM)is introduced in which Rectangular Filter Attention(RFA)and Square Filter Attention(SFA)are used to enhance direction-sensitive and local-structure-sensitive frequency-domain representations,respectively.Furthermore,a dual-domain loss is employed to jointly constrain spatial structural restoration and frequency-domain consistency,thereby improving restoration quality in complex scenes.Experimental results show that SynFSNet achieves superior performance on multiple synthetic and real-world datasets,verifying the effectiveness of collaborative spatial-frequency domain modeling for complex image deraining tasks.

关键词

图像去雨/频域特征/多尺度傅里叶融合/滤波器注意力/傅里叶变换

Key words

image deraining/frequency-domain feature/multi-scale Fourier fusion/filter attention/Fourier transform

分类

信息技术与安全科学

引用本文复制引用

李亚敏,向稳,刘钰婷,向尧..SynFSNet:频域与空间域协同建模的图像去雨网络[J].计算机工程,2026,52(8):58-70,13.

基金项目

湖北省自然科学基金面上项目(JCZRYB202501223). (JCZRYB202501223)

计算机工程

1000-3428

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