  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.
