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

• URL: http://arxiv.org/abs/2601.19506v2
• Date: Wed, 28 Jan 2026 06:02:25 GMT
• Title: Bridging Information Asymmetry: A Hierarchical Framework for Deterministic Blind Face Restoration
• Authors: Zhengjian Yao, Jiakui Hu, Kaiwen Li, Hangzhou He, Xinliang Zhang, Shuang Zeng, Lei Zhu, Yanye Lu,
• Abstract summary: We present textbfPrefRestore, a hierarchical framework that integrates discrete semantic logic with continuous texture generation.<n>Our methodology fundamentally addresses this information disparity through two complementary strategies.<n>Pref-Restore achieves state-of-the-art performance across synthetic and real-world benchmarks.
• Score: 31.878334664450776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• OMG-Agent: Toward Robust Missing Modality Generation with Decoupled Coarse-to-Fine Agentic Workflows [9.617220633655716]
  We present textbfunderlineOmni-textbfunderlineModality textbfunderlineGeneration Agent (textbfOMG-Agent)
  arXiv  Detail & Related papers  (2026-02-04T02:25:40Z)
• Implicit Neural Representation-Based Continuous Single Image Super Resolution: An Empirical Study [50.15623093332659]
  Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR)<n>We compare existing techniques across diverse settings and present aggregated performance results on multiple image quality metrics.<n>We examine a new loss function that penalizes intensity variations while preserving edges, textures, and finer details during training.
  arXiv  Detail & Related papers  (2026-01-25T07:09:20Z)
• Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding [54.05243949024302]
  Existing robust MLLMs rely on implicit training/adaptation that focuses solely on visual encoder generalization.<n>We propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains.<n>Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity.
  arXiv  Detail & Related papers  (2025-12-19T12:56:17Z)
• Knowledge Collapse in LLMs: When Fluency Survives but Facts Fail under Recursive Synthetic Training [2.094557609248011]
  Large language models increasingly rely on synthetic data due to human-written content scarcity.<n>Recursive training on model-generated outputs leads to model collapse, a degenerative process threatening factual reliability.
  arXiv  Detail & Related papers  (2025-09-05T04:29:15Z)
• How Far Are We from Generating Missing Modalities with Foundation Models? [49.425856207329524]
  We propose an agentic framework tailored for missing modality reconstruction.<n>Our method reduces FID for missing image reconstruction by at least 14% and MER for missing text reconstruction by at least 10% compared to baselines.
  arXiv  Detail & Related papers  (2025-06-04T03:22:44Z)
• AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting [10.175405673457892]
  We introduce a diffusion-based framework that integrates perceptual quality assessment directly into the generative restoration process.<n>AdaQual-Diff achieves visually superior restorations across diverse synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2025-04-17T03:08:27Z)
• Exploiting Diffusion Prior for Real-World Image Super-Resolution [75.5898357277047]
  We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. By employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model.
  arXiv  Detail & Related papers  (2023-05-11T17:55:25Z)
• DifFace: Blind Face Restoration with Diffused Error Contraction [62.476329680424975]
  DifFace is capable of coping with unseen and complex degradations more gracefully without complicated loss designs. It is superior to current state-of-the-art methods, especially in cases with severe degradations.
  arXiv  Detail & Related papers  (2022-12-13T11:52:33Z)
• Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality [44.47246905244631]
  We prove that any restoration algorithm that attains perfect quality must be a posterior sampler. We illustrate that while deterministic restoration algorithms may attain high quality, this can be achieved only by filling up the space of all possible source images.
  arXiv  Detail & Related papers  (2022-11-16T14:49:10Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction [54.06292969184476]
  We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Our approach employs semantic loss which captures the precise meaning of a logical sentence. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.
  arXiv  Detail & Related papers  (2021-03-20T00:16:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.