Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining

• URL: http://arxiv.org/abs/2509.09880v1
• Date: Thu, 11 Sep 2025 22:22:32 GMT
• Title: Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
• Authors: Yaşar Utku Alçalar, Junno Yun, Mehmet Akçakaya,
• Abstract summary: Diffusion/score-based models have emerged as powerful generative priors for solving inverse problems.<n>We propose Zero-shot Adaptive Diffusion Sampling (ZADS), a test-time optimization method that tunes fidelity weights across arbitrary noise schedules.<n>ZADS consistently outperforms both traditional compressed sensing and recent diffusion-based methods.
• Score: 4.511561231517167
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diffusion/score-based models have recently emerged as powerful generative priors for solving inverse problems, including accelerated MRI reconstruction. While their flexibility allows decoupling the measurement model from the learned prior, their performance heavily depends on carefully tuned data fidelity weights, especially under fast sampling schedules with few denoising steps. Existing approaches often rely on heuristics or fixed weights, which fail to generalize across varying measurement conditions and irregular timestep schedules. In this work, we propose Zero-shot Adaptive Diffusion Sampling (ZADS), a test-time optimization method that adaptively tunes fidelity weights across arbitrary noise schedules without requiring retraining of the diffusion prior. ZADS treats the denoising process as a fixed unrolled sampler and optimizes fidelity weights in a self-supervised manner using only undersampled measurements. Experiments on the fastMRI knee dataset demonstrate that ZADS consistently outperforms both traditional compressed sensing and recent diffusion-based methods, showcasing its ability to deliver high-fidelity reconstructions across varying noise schedules and acquisition settings.

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