Stabilizing Diffusion Posterior Sampling by Noise--Frequency Continuation

• URL: http://arxiv.org/abs/2602.00176v1
• Date: Fri, 30 Jan 2026 03:14:01 GMT
• Title: Stabilizing Diffusion Posterior Sampling by Noise--Frequency Continuation
• Authors: Feng Tian, Yixuan Li, Weili Zeng, Weitian Zhang, Yichao Yan, Xiaokang Yang,
• Abstract summary: At high noise, data-consistency gradients computed from inaccurate estimates can be geometrically incongruent with the posterior geometry.<n>We propose a noise--frequency Continuation framework that constructs a continuous family of intermediate posteriors whose likelihood enforces measurement consistency only within a noise-dependent frequency band.<n>Our method achieves state-of-the-art performance and improves motion deblurring PSNR by up to 5 dB over strong baselines.
• Score: 52.736416985173776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion posterior sampling solves inverse problems by combining a pretrained diffusion prior with measurement-consistency guidance, but it often fails to recover fine details because measurement terms are applied in a manner that is weakly coupled to the diffusion noise level. At high noise, data-consistency gradients computed from inaccurate estimates can be geometrically incongruent with the posterior geometry, inducing early-step drift, spurious high-frequency artifacts, plus sensitivity to schedules and ill-conditioned operators. To address these concerns, we propose a noise--frequency Continuation framework that constructs a continuous family of intermediate posteriors whose likelihood enforces measurement consistency only within a noise-dependent frequency band. This principle is instantiated with a stabilized posterior sampler that combines a diffusion predictor, band-limited likelihood guidance, and a multi-resolution consistency strategy that aggressively commits reliable coarse corrections while conservatively adopting high-frequency details only when they become identifiable. Across super-resolution, inpainting, and deblurring, our method achieves state-of-the-art performance and improves motion deblurring PSNR by up to 5 dB over strong baselines.

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