SynFSNet:频域与空间域协同建模的图像去雨网络OA

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.