###### Abstract  
Reference-based super-resolution (RefSR) enhances the detail restoration capability of low-resolution images (LR) by utilizing the details and texture information of external reference images (Ref). This study proposes a RefSR method based on hash adaptive matching and progressive multi-scale dynamic aggregation to improve the super-resolution reconstruction capability. Firstly, to address the issue of feature matching, this chapter proposes a hash adaptive matching module. On the basis of similarity calculation between traditional LR images and Ref images, self-similarity information of LR images is added to assist in super-resolution reconstruction. By dividing the feature space into multiple hash buckets through spherical hashing, the matching range is narrowed down from global search to local neighborhoods, enabling efficient matching in more informative regions. This not only retains global modeling capabilities, but also significantly reduces computational costs. In addition, a learnable similarity scoring function has been designed to adaptively optimize the similarity score between LR images and Ref images, improving matching accuracy. Secondly, in the process of feature transfer, this chapter proposes a progressive multi-scale dynamic aggregation module. This module utilizes dynamic decoupling filters to simultaneously perceive texture information in both spatial and channel domains, extracting key information more accurately and effectively suppressing irrelevant texture interference. In addition, this module enhances the robustness of the model to large-scale biases by gradually adjusting features at different scales, ensuring the accuracy of texture transfer. The experimental results show that this method achieves superior super-resolution reconstruction performance on multiple benchmark datasets. superc-resolution hash adaptive matching; dynamic aggregation 2025156821  
18 April 2  
a coarse-to-fine matching strategy, ensuring good detail recovery while reducing computational complexity. C\({}^{2}\)-Matching [6] improves the accuracy of feature matching by combining knowledge extraction and contrastive learning, further enhancing detail recovery. Although the existing RefSR methods have made some progress, the image block matching and pixel point matching strategies they adopt are limited to searching for the best texture transfer position in Ref images. However, rich and valuable texture information is not limited to a single location. Relying solely on a single match may result in insufficient transmission of texture information in Ref images, thereby affecting the final texture transfer effect. The root cause of this problem lies in the insufficient or even misuse of reference features, which limits the potential of matching strategies to improve the performance of RefSR methods. The matching based the RefSR method often ignores the self-similarity of natural images, which has been widely applied and proven to be highly effective in SISR tasks. The self-similarity of an image refers to the repeated appearance of image blocks within the same image. Based on this characteristic, non-local sparse prior methods [7; 8; 9] have been extensively studied in the field of SISR, which can search for similar features globally and perform weighted fusion to enhance feature expression ability. In the RefSR task, as the Ref image typically contains multiple corresponding regions similar to the target LR image, and the LR image itself also has similar regions, these regions can provide richer information than a single matching point, thereby facilitating higher-quality super-resolution reconstruction. To overcome these issues, we propose a RefSR method based on hash adaptive matching and progressive multi-scale dynamic aggregation. Firstly, to address the issue of feature matching, we introduced a hash adaptive matching module.