Image denoising—removal of additive white Gaussian noise from an image—is one of the
oldest and most studied problems in image processing. Extensive work over several …

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-
convexity modulus. Such regularizers give rise to variational denoisers that minimize a …

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 …

Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine
data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA …

Non-uniqueness and instability are characteristic features of image reconstruction methods.
As a result, it is necessary to develop regularization methods that can be used to compute …

Variational regularisation is the primary method for solving inverse problems, and recently
there has been considerable work leveraging deeply learned regularisation for enhanced …

Theoretical guarantees for the robust solution of inverse problems have important
implications for applications. To achieve both guarantees and high reconstruction quality …

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 …

This work explores plug-and-play algorithms for PET reconstruction, combining deep
learning with model-based variational methods. We aim to integrate classical convolutional …

Plug-and-play methods constitute a class of iterative algorithms for imaging inverse
problems where regularization is performed by an off-the-shelf Gaussian denoiser. These …