DifFace: Blind Face Restoration With Diffused Error Contraction.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Image Processing

Background:

• Deep learning methods for blind face restoration show success but struggle with complex, out-of-distribution degradations.
• Existing methods often require complex loss functions and extensive hyper-parameter tuning for optimal performance.

Purpose of the Study:

• To propose a novel method, DifFace, for blind face restoration that gracefully handles unseen and complex degradations.
• To develop a method that avoids complicated loss designs and laborious hyper-parameter tuning.

Main Methods:

• DifFace establishes a posterior distribution from low-quality (LQ) to high-quality (HQ) faces.
• It utilizes a transition distribution from LQ images to an intermediate state of a pre-trained diffusion model.
• The method recursively applies the pre-trained diffusion model to generate the HQ target, using a restoration backbone trained with L1 loss.

Main Results:

• DifFace demonstrates superior performance compared to state-of-the-art methods, particularly with severe degradations.
• The transition distribution effectively contracts errors from the restoration backbone, enhancing robustness to unknown degradations.
• The method avoids the cumbersome training processes common in existing approaches.

Conclusions:

• DifFace offers a robust and effective solution for blind face restoration, excelling in challenging degradation scenarios.
• The proposed method simplifies the restoration process by eliminating the need for complex loss functions and tuning.
• DifFace represents a significant advancement in handling complex and unseen degradations in face image restoration.