发表机构
Nankai University; Tsinghua University（南开大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出频率解耦后验引导，分离频率激活与衰减正则化，结合渐进频率调度和核导出的衰减图，并引入局部轨迹正则化，在非盲去模糊中实现强PSNR/SSIM性能。
AI 中文摘要
预训练的扩散模型为图像恢复中的免训练后验采样提供了强大的图像先验。为了引导这一采样过程，频率感知方法逐步在频带间整合测量信息，促进从粗到细的重建。然而，现有方法通常不明确地将频率激活与退化引起的衰减分离，导致非激活频率间的衰减差异未能得到充分建模。在本工作中，我们提出频率解耦后验引导，将频率激活与衰减感知的谱正则化分离。具体而言，一个从低频到高频的渐进式调度确定活跃测量频带，而由核导出的衰减图定义非激活分量上的选择性谱先验。为稳定采样过程，我们还引入局部轨迹正则化器，抑制空间上不规则的从状态到干净图像的偏差。对于固定的端点能量，我们提供一种KL正则化的路径空间解释。在实践中，我们通过Tweedie插件近似进行局部能量校正来构造时间依赖的引导。在自然图像基准上的实验表明，在具有挑战性的非盲去模糊设置中，即使在更高的测量噪声水平下，也展现出强大的PSNR和SSIM性能。
英文摘要
Pretrained diffusion models provide powerful image priors for training-free posterior sampling in image restoration. To guide this sampling process, frequency-aware methods progressively incorporate measurement information across frequency bands, facilitating coarse-to-fine reconstruction. However, existing methods typically do not explicitly separate frequency activation from degradation-induced attenuation, leaving attenuation differences among inactive frequencies insufficiently modeled. In this work, we propose frequency-decoupled posterior guidance to separate frequency activation from attenuation-aware spectral regularization. Specifically, a progressive low-to-high frequency schedule determines the active measurement band, while a kernel-derived attenuation map defines a selective spectral prior over inactive components. To stabilize the sampling process, we also introduce a local trajectory regularizer that suppresses spatially irregular state-to-clean deviations. For a fixed endpoint energy, we provide a KL-regularized path-space interpretation. In practice, we construct time-dependent guidance through local energy corrections using a Tweedie plug-in approximation. Experiments on natural-image benchmarks demonstrate strong PSNR and SSIM performance across challenging non-blind deblurring settings, even at higher measurement noise levels.
Comments31 pages, 12 figures. Project page: https://github.com/Sea-serpents/frequency-decoupled-diffusion-guidance
