Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration

TL;DR

This paper introduces Pref-Restore, a hierarchical framework that combines semantic logic and reinforcement learning to achieve deterministic, high-quality blind face restoration, overcoming the limitations of stochastic generative methods.

Contribution

The paper proposes a novel hierarchical framework integrating semantic augmentation and reinforcement learning to produce deterministic face restoration results, reducing uncertainty and hallucinations.

Findings

Achieves state-of-the-art performance on benchmarks.

Reduces solution entropy for more reliable restoration.

Effectively aligns outputs with human preferences.

Abstract

Blind face restoration remains a persistent challenge due to the inherent ill-posedness of reconstructing holistic structures from severely constrained observations. Current generative approaches, while capable of synthesizing realistic textures, often suffer from information asymmetry -- the intrinsic disparity between the information-sparse low quality inputs and the information-dense high quality outputs. This imbalance leads to a one-to-many mapping, where insufficient constraints result in stochastic uncertainty and hallucinatory artifacts. To bridge this gap, we present \textbf{Pref-Restore}, a hierarchical framework that integrates discrete semantic logic with continuous texture generation to achieve deterministic, preference-aligned restoration. Our methodology fundamentally addresses this information disparity through two complementary strategies: (1) Augmenting Input Density: We employ an auto-regressive integrator to reformulate textual instructions into dense latent queries, injecting high-level semantic stability to constrain the degraded signals; (2) Pruning Output Distribution: We pioneer the integration of on-policy reinforcement learning directly into the diffusion restoration loop. By transforming human preferences into differentiable constraints, we explicitly penalize stochastic deviations, thereby sharpening the posterior distribution toward the desired high-fidelity outcomes. Extensive experiments demonstrate that Pref-Restore achieves state-of-the-art performance across synthetic and real-world benchmarks. Furthermore, empirical analysis confirms that our preference-aligned strategy significantly reduces solution entropy, establishing a robust pathway toward reliable and deterministic blind restoration.