## 2 Related Works
### Reference-Based Image Super-Resolution  
Unlike traditional SISR methods, RefSR improves detail restoration by introducing additional high-resolution reference (Ref) images to reconstruct finer details. Reference images typically contain content related to LR images, providing authentic detail information for the reconstruction process. In 2018, [PERSON] et al. [1] proposed CrossNet, which aligns Ref and LR images through optical flow distortion. Although algorithms based on optical flow can alleviate the problem of image differences caused by multiple perspectives and scales, their computational complexity is high, and there are still bottlenecks in accurate optical flow prediction. To address this issue, [PERSON] et al. [2] proposed a network structure called SSEN based on deformable convolution. The network adopts deformable convolution operation to construct feature association mapping between LR images and Ref images, and designs a dynamic offset estimation module to adaptively learn the deformation parameters of the convolution kernel. On the other hand, inspired by image style transfer, [PERSON] et al. [3] proposed an end-to-end deep learning model SRNTT, which restores LR images through neural texture transfer. This network performs dense block matching on image features and dynamically fuses the texture information of Ref images into LR images by analyzing the similarity of texture features, effectively filling the texture gaps in SR images. [PERSON] et al. [4] pointed out that the SRNTT method ignores global information. They first introduced a transformer into RefSR and designed a TTSR model that mines deep feature correspondence relationships through attention mechanisms. In 2021, [PERSON] et al. [5] proposed MASA, which uses a hierarchical enumeration matching scheme and a coarse to fine matching strategy to reduce computational overhead. However, due to the resolution difference between LR and Ref images, MASA still has limitations in repairing severely damaged textures, especially for non-local textures with low similarity, which typically provide more accurate detail information. In the same year, [PERSON] et al. [6] introduced C\({}^{2}\)-Matching, which combines knowledge extraction and contrastive learning to improve matching robustness and enhance feature alignment between LR and Ref images. In 2022, [PERSON] et al. [24] used wavelet transform to decompose image features into high-frequency and low-frequency sub-bands, further improving the efficiency of texture information transmission. In addition, [PERSON] et al. [25] proposed a decoupling framework that divides the RefSR problem into two parts--super-resolution reconstruction and texture transfer--effectively alleviating issues related to insufficient or misused references. [PERSON] et al. [26] proposed a reciprocal learning framework that uses RefSR results as a new reference for the second RefSR and creates a feedback loop to improve performance. [PERSON] et al. [27] introduced the LMR framework, which integrates features from multiple reference images and constructs a large-scale super-resolution dataset. However, due to the size of the dataset, this is time-consuming. [PERSON] et al. [28] proposed MCM, a model that finds multiple corresponding points for each block or pixel in Ref images, utilizing more texture information and improving performance.