Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes.

翻译：近年来，基于参考的图像超分辨率（Ref-SR）技术蓬勃发展。通过将高分辨率（HR）参考图像引入单图像超分辨率（SISR）方法，该长期存在的病态问题借助从参考图像迁移的纹理得到了缓解。尽管定量与定性结果的显著提升已证实Ref-SR方法的优越性，但纹理迁移前的未对齐现象表明性能仍有提升空间。现有方法往往忽视对比背景下细节的重要性，因此未能充分利用低分辨率（LR）图像中包含的信息。本文提出一种基于参考超分辨率的细节增强框架（DEF），该框架引入扩散模型来生成并增强LR图像中的潜在细节。若参考图像中存在对应部分，我们的方法可促进严格对齐；若参考图像缺失对应部分，则能确保基础性改进，同时避免参考图像的影响。大量实验表明，所提方法在保持可比数值结果的同时，实现了更优的视觉效果。