Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Deep neural networks have provided state-of-the-art solutions for problems such as image
denoising, which implicitly rely on a prior probability model of natural images. Two recent …

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) …

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 …

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 …