Quality-Assisted Domain Transfer for Fast Face Super-Resolution

Face super-resolution (FSR) is a challenging task, especially when the low-resolution (LR) image does not have sufficient information to build a high-resolution (HR) image. In this paper, we propose a quality-assisted domain transfer framework that uses a two-stage pipeline for efficient face SR. Instead of treating all LR images uniformly, we use a quality evaluator to classify images in different LR domains with distinct quality levels and determine the starting points for domain transfer adaptively. We further design a new residual domain learning strategy that integrates the LR domain with learned residual information. This approach allows us to avoid the correction formulation in diffusion models and the momentum correction in the originally proposed domain transfer approach and enables the model to learn the transformation from the LR domain to the HR domain gradually while preserving content information. The experimental results demonstrate competitive performance with the best inference speed among all approaches.