MAP Estimation with Denoisers: Convergence Rates and Guarantees

Scott Pesme, Giacomo Meanti, Michael Arbel, Julien Mairal

Keywords: MAP estimation, proximal operator, denoiser model, plug and play methods

Abstract: Denoiser models have become powerful tools for inverse problems, enabling the use of pretrained networks to approximate the score of a smoothed prior distribution. These models are often used in heuristic iterative schemes aimed at solving Maximum a Posteriori (MAP) optimisation problems, where the proximal operator of the negative log-prior plays a central role. In practice, this operator is intractable, and practitioners plug in a pretrained denoiser as a surrogate—despite the lack of general theoretical justification for this substitution. In this work, we show that a simple algorithm, closely related to several used in practice, provably converges to the proximal operator under a log-concavity assumption on the prior $p$. We show that this algorithm can be interpreted as a gradient descent on smoothed proximal objectives. Our analysis thus provides a theoretical foundation for a class of empirically successful but previously heuristic methods

Primary Area: Optimization (e.g., convex and non-convex, stochastic, robust)

Submission Number: 15639