🤖 AI Summary
Diffusion models for accelerating inverse problem reconstruction (e.g., MRI) suffer from poor generalization—especially under fast sampling and few-step denoising—due to heavy reliance on manually tuned data fidelity weights. To address this, we propose Zero-shot Adaptive Denoising Sampling (ZADS), the first method enabling *test-time automatic fidelity weight optimization without retraining the diffusion prior*. Built upon an unrolled sampler architecture, ZADS jointly optimizes fidelity weights and the denoising trajectory in a self-supervised manner, using only undersampled measurements. It supports arbitrary numbers of sampling steps and non-uniform time schedules. Evaluated on the fastMRI knee dataset, ZADS significantly outperforms compressed sensing and state-of-the-art diffusion-based methods, achieving both high reconstruction fidelity and robustness across diverse imaging conditions—thereby overcoming the fundamental limitations of fixed or heuristic weight schemes.
📝 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.