Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

Recently, diffusion models have been used to solve various inverse problems in an
unsupervised manner with appropriate modifications to the sampling process. However, the …

Recent works on plug-and-play image restoration have shown that a denoiser can implicitly
serve as the image prior for model-based methods to solve many inverse problems. Such a …

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 …

While deep neural networks (DNN) based single image super-resolution (SISR) methods
are rapidly gaining popularity, they are mainly designed for the widely-used bicubic …

This review provides an introduction to—and overview of—the current state of the art in
neural‐network based regularization methods for inverse problems in imaging. It aims to …

In this work we introduce a novel stochastic algorithm dubbed SNIPS, which draws samples
from the posterior distribution of any linear inverse problem, where the observation is …

Abstract Plug-and-play (PnP) is a non-convex framework that integrates modern denoising
priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal …

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 …