Measurement-Guided Consistency Model Sampling for Inverse Problems

• URL: http://arxiv.org/abs/2510.02208v1
• Date: Thu, 02 Oct 2025 16:53:07 GMT
• Title: Measurement-Guided Consistency Model Sampling for Inverse Problems
• Authors: Amirreza Tanevardi, Pooria Abbas Rad Moghadam, Sajjad Amini,
• Abstract summary: Consistency models enable high-quality generation in a single or only a few steps.<n>We present a modified consistency sampling approach tailored for inverse problem reconstruction.
• Score: 2.217547045999963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have become powerful generative priors for solving inverse imaging problems, but their reliance on slow multi-step sampling limits practical deployment. Consistency models address this bottleneck by enabling high-quality generation in a single or only a few steps, yet their direct adaptation to inverse problems is underexplored. In this paper, we present a modified consistency sampling approach tailored for inverse problem reconstruction: the sampler's stochasticity is guided by a measurement-consistency mechanism tied to the measurement operator, which enforces fidelity to the acquired measurements while retaining the efficiency of consistency-based generation. Experiments on Fashion-MNIST and LSUN Bedroom datasets demonstrate consistent improvements in perceptual and pixel-level metrics, including Fr\'echet Inception Distance, Kernel Inception Distance, peak signal-to-noise ratio, and structural similarity index measure, compared to baseline consistency sampling, yielding competitive or superior reconstructions with only a handful of steps.

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