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

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 introduce a new paradigm for solving regularized variational problems. These are
typically formulated to address ill-posed inverse problems encountered in signal and image …

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 …

We introduce a new class of iterative image reconstruction algorithms for radio
interferometry, at the interface of convex optimization and deep learning, inspired by plug …

Lipschitz-constrained neural networks have several advantages over unconstrained ones
and can be applied to a variety of problems, making them a topic of attention in the deep …

Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful
generative models. This Element provides a unified framework to handle these approaches …

Although Lipschitz-constrained neural networks have many applications in machine
learning, the design and training of expressive Lipschitz-constrained networks is very …

Exploiting image patches instead of whole images has proved to be a powerful approach to
tackling various problems in image processing. Recently, Wasserstein patch priors (WPPs) …

Motivated by classical work on the numerical integration of ordinary differential equations we
present a ResNet-styled neural network architecture that encodes non-expansive (1 …