用确定性流程逆转图像退化，四步内完成修复且效果领先。
Reversing Flow for Image Restoration
- 将退化过程建模为确定性路径，避免随机性带来的复杂与低效。
- 仅需少于4次采样即可完成修复，速度远超现有生成模型。
- 适合追求高效实用的图像修复场景，尤其适用于真实应用。
图像恢复旨在通过逆向退化过程，从低质量（LQ）图像中恢复高质量（HQ）图像。现有生成模型（如扩散模型和基于得分的模型）通常将退化过程视为随机变换，导致效率低下且结构复杂。本文提出ResFlow，一种新型图像恢复框架，将退化过程建模为连续归一化流中的确定性路径。ResFlow引入辅助过程以消除高质量图像预测中的不确定性，实现退化过程的可逆建模。该方法采用保熵流路径，并通过匹配速度场学习增强的退化流。大量实验表明，ResFlow在多个图像恢复基准上达到最优性能，仅需少于四次采样即可完成修复，为实际应用提供了高效可行的解决方案。
原文摘要 · Abstract (English)
Image restoration aims to recover high-quality (HQ) images from degraded low-quality (LQ) ones by reversing the effects of degradation. Existing generative models for image restoration, including diffusion and score-based models, often treat the degradation process as a stochastic transformation, which introduces inefficiency and complexity. In this work, we propose ResFlow, a novel image restoration framework that models the degradation process as a deterministic path using continuous normalizing flows. ResFlow augments the degradation process with an auxiliary process that disambiguates the uncertainty in HQ prediction to enable reversible modeling of the degradation process. ResFlow adopts entropy-preserving flow paths and learns the augmented degradation flow by matching the velocity field. ResFlow significantly improves the performance and speed of image restoration, completing the task in fewer than four sampling steps. Extensive experiments demonstrate that ResFlow achieves state-of-the-art results across various image restoration benchmarks, offering a practical and efficient solution for real-world applications.
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