Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction

Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors.

In practice, these denoisers are often deployed outside their training domains.

Existing analyses establish convergence under structural assumptions on the deployed denoiser, such as requiring it to be a proximal map or a contraction.

However, they do not measure how domain mismatch affects convergence of PnP-PGD.

We define this effect as \emph{proximal mismatch}: the discrepancy between a deployed denoiser $\widehat{\mathsf D}$ and a target-domain reference map $\mathsf D_\star=\operatorname{prox}_{R_\star}$ associated with the underlying regularizer $R_\star$.

Under this mismatch, each denoising update becomes an inexact proximal step for the target objective.

We further derive a stationarity bound that decays at a rate of $\mathcal{O}(1/K)$, with an additive term proportional to the average squared proximal mismatch.

This result motivates adaptation via proximal matching rather than MSE-based adaptation alone.

We study this approach with two established denoiser families: learned proximal networks and gradient-step denoisers.

Experiments on Gaussian deblurring and super-resolution under substantial domain shift show that proximal matching adaptation improves reconstruction quality significantly over MSE-based adaptation, yielding the largest numerical gains in the few-shot regime.