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.

通过引入扩散模型来生成和增强低分辨率图像中的细节，本文提出了一种用于基于参考的超分辨率的细节增强框架（DEF），能够促进对齐，同时避免参考图像的影响，实验结果表明该方法在视觉效果上具有优势同时保持可比较的数值结果。