基于高频融合注意力的轻量级图像超分辨率重建OA

Existing image super-resolution algorithms face issues such as high computational cost,limited perception of local high-frequency textures,and insufficient extraction of key information.To address these challenges,this paper proposes a lightweight image super-resolution reconstruction network based on high-frequency fusion attention.First,a Dual-Path Enhancement module is designed within the network architecture to explicitly decouple features into parallel high-frequency and low-frequency branches.The low-frequency branch captures smooth structures,while the high-frequency branch employs learnable high-pass filters to accurately extract edges and textures.Second,a spatial refinement module is introduced,utilizing directionally separated convolutions to enhance local features and generate queries rich in spatial information.Finally,an efficient asymmetric frequency-aware attention mechanism is designed to achieve effective interaction between local and global information.This mechanism uses the local information output from the spatial refinement module as a query to dynamically retrieve complementary information across the entire image,effectively overcoming the limitation of local windows on long-range dependencies.Experiments on the Urban100 dataset demonstrate that at×4 magnification scales,the proposed algorithm achieves an average PSNR improvement of 0.53 dB and an average SSIM improvement of 0.014 7 compared to other models,while running at only 14%of SwinIR's computational time.Regarding visual quality in image reconstruction,the model generates sharper edges and more realistic textures,fully validating its effectiveness and advanced capabilities in image reconstruction while maintaining lightweight efficiency.