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Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration
Summary
Pref-Restore is a new hierarchical framework for deterministic blind face restoration (BFR). It improves identity preservation and reduces uncertainty in reconstructing faces from degraded images.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Processing
Background:
- Blind face restoration (BFR) is challenging due to ill-posed reconstruction from limited data.
- Generative models produce realistic details but struggle with identity consistency in BFR.
Purpose of the Study:
- To introduce Pref-Restore, a hierarchical framework for deterministic blind face restoration.
- To enhance identity preservation and reduce restoration uncertainty in BFR.
Main Methods:
- Semantic Information Augmentation: Auto-regressive branch uses image/text cues for structured tokens.
- Texture-level Fidelity Alignment: Diffusion generator trained with semantic anchors for identity details.
- Fidelity-constrained Preference Optimization: Face-aware reward refines diffusion, balancing quality and fidelity.
Main Results:
- Pref-Restore achieves state-of-the-art performance on synthetic and real-world benchmarks.
- Demonstrates improved identity-sensitive fidelity and lower restoration uncertainty.
- Ablation studies confirm the effectiveness of the hierarchical design and its components.
Conclusions:
- The proposed hierarchical framework effectively addresses limitations in blind face restoration.
- Pref-Restore offers a robust and deterministic approach for high-fidelity face reconstruction.
- The method shows significant improvements in identity preservation and restoration consistency.
