  Reference-based super-resolution (RefSR) has gained considerable success in
the field of super-resolution with the addition of high-resolution reference
images to reconstruct low-resolution (LR) inputs with more high-frequency
details, thereby overcoming some limitations of single image super-resolution
(SISR). Previous research in the field of RefSR has mostly focused on two
crucial aspects. The first is accurate correspondence matching between the LR
and the reference (Ref) image. The second is the effective transfer and
aggregation of similar texture information from the Ref images. Nonetheless, an
important detail of perceptual loss and adversarial loss has been
underestimated, which has a certain adverse effect on texture transfer and
reconstruction. In this study, we propose a feature reuse framework that guides
the step-by-step texture reconstruction process through different stages,
reducing the negative impacts of perceptual and adversarial loss. The feature
reuse framework can be used for any RefSR model, and several RefSR approaches
have improved their performance after being retrained using our framework.
Additionally, we introduce a single image feature embedding module and a
texture-adaptive aggregation module. The single image feature embedding module
assists in reconstructing the features of the LR inputs itself and effectively
lowers the possibility of including irrelevant textures. The texture-adaptive
aggregation module dynamically perceives and aggregates texture information
between the LR inputs and the Ref images using dynamic filters. This enhances
the utilization of the reference texture while reducing reference misuse. The
source code is available at https://github.com/Yi-Yang355/FRFSR.

Computer Vision and Pattern Recognition