通过调控噪声频率提升图像修复的保真度与视觉质量
NFCDS: A Plug-and-Play Noise Frequency-Controlled Diffusion Sampling Strategy for Image Restoration
- 在频域设计滤波器，逐步抑制低频噪声、保留高频细节
- 无需额外训练，实现高保真与高感知质量的平衡
- 可即插即用，适用于多种零样本图像修复任务
基于扩散采样的即插即用（PnP）方法虽能生成高感知质量的图像，但常因反向扩散过程引入噪声而导致数据保真度下降。为此，我们提出噪声频率控制扩散采样（NFCDS），一种针对反向扩散噪声的谱调制机制。研究表明，保真度与感知质量的权衡可归因于噪声频率：低频成分导致模糊并降低保真度，而高频成分促进细节生成。基于此，我们设计了一个傅里叶域滤波器，逐步抑制低频噪声并保留高频内容。该可控优化将数据一致性先验直接注入采样过程，实现快速收敛至既高保真又具感知说服力的结果，且无需额外训练。作为PnP模块，NFCDS可无缝集成至现有基于扩散的修复框架，在多种零样本任务中显著改善保真度-感知平衡。
原文摘要 · Abstract (English)
Diffusion sampling-based Plug-and-Play (PnP) methods produce images with high perceptual quality but often suffer from reduced data fidelity, primarily due to the noise introduced during reverse diffusion. To address this trade-off, we propose Noise Frequency-Controlled Diffusion Sampling (NFCDS), a spectral modulation mechanism for reverse diffusion noise. We show that the fidelity-perception conflict can be fundamentally understood through noise frequency: low-frequency components induce blur and degrade fidelity, while high-frequency components drive detail generation. Based on this insight, we design a Fourier-domain filter that progressively suppresses low-frequency noise and preserves high-frequency content. This controlled refinement injects a data-consistency prior directly into sampling, enabling fast convergence to results that are both high-fidelity and perceptually convincing--without additional training. As a PnP module, NFCDS seamlessly integrates into existing diffusion-based restoration frameworks and improves the fidelity-perception balance across diverse zero-shot tasks.
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