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 …

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 …

Abstract Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-
used frameworks for solving imaging inverse problems by computing fixed-points of …

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 …

The emergence of deep-learning-based methods to solve image-reconstruction problems
has enabled a significant increase in quality. Unfortunately, these new methods often lack …

There has been significant recent interest in the use of deep learning for regularizing
imaging inverse problems. Most work in the area has focused on regularization imposed …

Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal
algorithms, for example, the alternating direction method of multipliers (ADMM), with …

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 …

Self-supervised learning (SSL) has led to important breakthroughs in computer vision by
allowing learning from large amounts of unlabeled data. As such, it might have a pivotal role …

Denoising, the process of reducing random fluctuations in a signal to emphasize essential
patterns, has been a fundamental problem of interest since the dawn of modern scientific …