In recent years, deep learning has achieved remarkable empirical success for image
reconstruction. This has catalyzed an ongoing quest for the precise characterization of the …

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 …

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 …

Diffusion models have emerged as a key pillar of foundation models in visual domains. One
of their critical applications is to universally solve different downstream inverse tasks via a …

Since the seminal work of Venkatakrishnan, Bouman, and Wohlberg [Proceedings of the
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …

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

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 …

We propose a new approach for learning end-to-end reconstruction operators based on
unpaired training data for ill-posed inverse problems. The proposed method combines the …