DIFBFSR: BLIND FACE SUPER-RESOLUTION VIA CONDITIONAL DIFFUSION CONTRACTION

Blind Face Super -Resolution (BFSR) has recently gained widespread attention, which aims to super -resolve Low -Resolution (LR) face images with complex unknown degradation to High -Resolution (HR) face images. However, existing BFSR methods suffer from two major limitations. First, most of them are trained on synthetic degradation data pairs with pre -defined degradation models, which leads to poor performance due to the degradation mismatch between other unknown complex degradations in real -world scenarios. Second, some methods rely on hand-crafted face priors as constraints, such as facial landmarks and parsing maps, which require additional callouts and laborious hyperparameter tuning for real cases. To tackle these issues, we propose a simple and effective self -supervised cooperative learning framework via a conditional diffusion contraction method for BFSR, dubbed DifBFSR, which establishes the posterior distribution of HR images from degraded LR images with unknown degradation via a powerful diffusion model without expensive supervised training or additional constraint design. Specifically, we first transform the degraded LR face image to an intermediate HR face prediction with degradation -invariant by a simple Super -Resolution module (SRM), which only relies on self -supervised optimization. To enhance the face pre diction, we propose a Contraction Filter Module (CFM) to gradually contract the restoration error by adaptive dynamic filtering, which efficiently leverages rich na- ture face prior encapsulated in the pre -trained diffusion model through conditional posterior sampling. Finally, by combining the SRM, CFM, and diffusion model in a self -supervised cooperative learning framework, DifBFSR can robustly handle unknown complex degradations, which favorably avoids the cumbersome training and parameter tuning. Extensive qualitative and quantitative experiments on com- plex degraded synthetic and real -world datasets show that our method outperforms state-of-the-art BFSR methods.