用流匹配+掩码引导，快速修复图像且不模糊。
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
- 用退化掩码指导流匹配采样，无需训练。
- 处理速度更快，视觉质量优于扩散模型和现有流方法。
- 适合需要快速修复的自然与医学图像任务。
流匹配作为一种新兴生成方法，解决了当前扩散模型采样时间长的问题，支持更灵活的轨迹设计，同时保持高质量图像生成能力，因此适用于图像修复任务中的生成先验。尽管已有基于流模型的方法在修复任务中表现良好，但仍存在处理时间长或结果过度平滑的问题。为此，我们提出 Restora-Flow，一种无需训练的方法，通过退化掩码引导流匹配采样，并引入轨迹修正机制以保证与退化输入的一致性。我们在包含自然图像和医学图像的多个数据集上，针对基于掩码退化的修复任务（如补全、超分辨率、去噪）进行了评估。实验表明，该方法在感知质量和处理速度方面均优于扩散模型及现有的流匹配基线方法。
原文摘要 · Abstract (English)
Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible trajectory design, while maintaining high-quality image generation. This capability makes it suitable as a generative prior for image restoration tasks. Although current methods leveraging flow models have shown promising results in restoration, some still suffer from long processing times or produce over-smoothed results. To address these challenges, we introduce Restora-Flow, a training-free method that guides flow matching sampling by a degradation mask and incorporates a trajectory correction mechanism to enforce consistency with degraded inputs. We evaluate our approach on both natural and medical datasets across several image restoration tasks involving a mask-based degradation, i.e., inpainting, super-resolution and denoising. We show superior perceptual quality and processing time compared to diffusion and flow matching-based reference methods.
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