Abstract Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed
image inverse problems. PnP methods are obtained by using deep Gaussian denoisers …

This paper presents a new convergent Plug-and-Play (PnP) algorithm. PnP methods are
efficient iterative algorithms for solving image inverse problems formulated as the …

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse
problems by using advanced denoisers within an iterative algorithm. Recent experimental …

In this work, we present new proofs of convergence for plug-and-play (PnP) algorithms. PnP
methods are efficient iterative algorithms for solving image inverse problems where …

Abstract Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative
proximal algorithms by replacing a proximal operator by a denoising operation. When …

Ill-posed linear inverse problems are frequently encountered in image reconstruction tasks.
Image reconstruction methods that combine the Plug-and-Play (PnP) priors framework with …

Abstract Plug-and-play (PnP) prior is a well-known class of methods for solving imaging
inverse problems by computing fixed-points of operators combining physical measurement …

Inverse problems in image processing are typically cast as optimization tasks, consisting of
data fidelity and stabilizing regularization terms. A recent regularization strategy of great …

Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image
inverse problems by combining a physical model and a deep neural network for …

In recent years there has been increasing interest in leveraging denoisers for solving
general inverse problems. Two leading frameworks are regularization-by-denoising (RED) …