Abstract
Diffusion and flow-based generative models have shown strong potential for image restoration. However, image denoising under unknown and varying noise conditions remains challenging, because the learned vector fields may become inconsistent across different noise levels, leading to degraded restoration quality under mismatch between training and inference. To address this issue, we propose a quantitative flow matching framework for adaptive image denoising. The method first estimates the input noise level from local pixel statistics, and then uses this quantitative estimate to adapt the inference trajectory, including the starting point, the number of integration steps, and the step-size schedule. In this way, the denoising process is better aligned with the actual corruption level of each input, reducing unnecessary computation for lightly corrupted images while providing sufficient refinement for heavily degraded ones. By coupling quantitative noise estimation with noise-adaptive flow inference, the proposed method improves both restoration accuracy and inference efficiency. Extensive experiments on natural, medical, and microscopy images demonstrate its robustness and strong generalization across diverse noise levels and imaging conditions.
Abstract (translated)
扩散模型和基于流的生成模型在图像复原领域展现出巨大潜力。然而，在未知且变化的噪声条件下进行图像去噪仍具挑战性，因为学习到的向量场可能在不同噪声水平下变得不一致，导致训练与推理不匹配时复原质量下降。针对这一问题，我们提出了一种用于自适应图像去噪的定量流匹配框架。该方法首先从局部像素统计中估计输入噪声水平，然后利用这一定量估计来调整推理轨迹，包括起点、积分步数以及步长调度。通过这种方式，去噪过程能更好地与每张输入图像的实际退化程度对齐，对轻度退化图像减少不必要的计算，同时对重度退化图像提供充分的细化处理。通过将定量噪声估计与噪声自适应流推理相结合，所提方法在复原精度和推理效率上均得到提升。在自然图像、医学图像及显微图像上的大量实验表明，该方法在不同噪声水平和成像条件下具有鲁棒性和强泛化能力。
URL
https://arxiv.org/abs/2604.02392
