UW-Madison's SGPnP Beats Diffusion Solvers on Severely Corrupted MRI Scans

Plug-and-play (PnP) methods reconstruct images from incomplete or corrupted measurements by alternating between a data-consistency update and a learned denoiser. Data consistency enforces what the scanner or camera measured; the denoiser supplies an image prior, replacing the proximal operator that a conventional optimizer would use. This pattern supports MRI reconstruction, deblurring, super-resolution, and inpainting without requiring an explicit formula for the prior.

A new study from researchers at UW-Madison, Washington University in St. Louis, and Los Alamos National Laboratory finds that conventional PnP methods lose accuracy as measurements become sparse or corruption grows severe. Their Stochastic Generative Plug-and-Play method, or SGPnP, combines a pretrained score-based diffusion model with fresh Gaussian noise at each iteration. It competes with specialized diffusion solvers while preserving the existing PnP optimization loop. The authors have released the source code.

Hard corruptions expose PnP’s blind spot

Standard PnP denoisers are usually trained to remove low-level additive Gaussian noise. Intermediate iterates from difficult inverse problems look different: large masks create block-shaped gaps, undersampled MRI produces aliasing, and deblurring leaves structured residuals. This distribution shift gives the denoiser inputs that differ sharply from its training data.

Low-noise denoisers also lack the broad generative behavior needed to infer large missing regions. Re-noising methods such as SNORE reduce the distribution mismatch by adding noise before denoising, but their low-noise models provide limited gains on strongly underdetermined tasks. Score-based diffusion models cover a much wider range of corruption levels and learn image structure at multiple scales.