SSTRN: Semantic Style Transfer Reference Network for Face Super-Resolution
Saghar Farhangfar, Aryaz Baradarani, Mohammad Ali Balafar, Mohammad Asadpour · 2022
Reference super-resolution (RefSR) has achieved promising results in the single image super-resolution (SISR) field by providing additional details from the reference images. Existing RefSR methods usually tend to extract similar or aligned features from reference images to further enhance the resolution of the final result. Therefore, the efficiency of RefSR models highly depends on the conformity between extracted features from the low-resolution (LR) and reference images. In this paper, we propose a new reference image generation scheme via semantic style transfer to unleash our model from relevant feature extraction computations. The generated reference images have the most content similarity and identical alignment with the LR input that compensates for the lost details of the LR images. Despite previous RefSR methods that rely on extracting and transferring texture information from the reference image to LR input, provided reference images are enriched with the style information of high-resolution (HR) images. Extensive experiments indicate the effectiveness of the proposed reference images.