<p>Reference-based Image Super-Resolution (RefSR) enhances low-resolution (LR) images by transferring texture details from external reference (Ref) images. Existing methods still face challenges in establishing correspondences between LR and Ref images: patch matching and deformable convolution approaches struggle with long-distance correspondences and fail to effectively utilize all potential reference information; Transformer-based methods, while capable of establishing global dependencies between LR and Ref images, suffer from high computational complexity. To address these issues, this paper proposes a novel RefSR method that combines the local precise alignment capability of deformable convolution with the Mamba model’s efficient long-distance dependency modeling through an alternating scanning strategy, achieving efficient and accurate reference feature alignment. Additionally, we design a wavelet-based feature modulation module to enhance important high-frequency texture features from Ref images and a reference feature fusion module capable of adaptive filtering and deep integration of reference features. Experiments on multiple benchmark datasets validate the effectiveness of our proposed method both quantitatively and qualitatively.</p>