UniFRD: A Unified Method for Facial Image Restoration Based on Diffusion Probabilistic Model

Abstract

This paper presents a Unified Facial image and video Restoration method based on the Diffusion probabilistic model (UniFRD), designed to effectively address both single- and multi-type image degradation. The noise predictor in UniFRD consists of a ViT-based encoder and a novel Separation Fusion Decoding Module (SFDM). The flexible feature optimization strategy allows for decoding complex conditional noise without being limited by degradation patterns. Specifically, SFDM adjusts and refines the channel correlation and expressive power of high-dimensional features step by step, enabling the network to more accurately perceive and enhance the interaction between posterior probabilities and conditional inputs. This process is crucial for improving the visual quality and stability of the restoration results. Extensive experiments demonstrate that even when facial images suffer from both pixel-level and image-level degradation, UniFRD can still guarantee the restoration of rich details and maintain attribute consistency. In summary, compared to existing methods, the solution proposed in this study for facial restoration tasks offers greater generality and adaptability. Moreover, it has high practical value for applications involving faces in complex and unconstrained outdoor scenarios.