Image deblurring is an ill-posed problem with multiple plausible solutions for a given input
image. However, most existing methods produce a deterministic estimate of the clean image …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

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 …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration that
avoids the so-called" regression to the mean" effect and produces more realistic and …

Generative models (eg, GANs, diffusion models) learn the underlying data distribution in an
unsupervised manner. However, many applications of interest require sampling from a …

In generative compressed sensing (GCS), we want to recover a signal $\mathbf
{x^*}\in\mathbb {R}^ n $ from $ m $ measurements ($ m\ll n $) using a generative prior …

Latest diffusion-based methods for many image restoration tasks outperform traditional
models, but they encounter the long-time inference problem. To tackle it, this paper …

To overcome topological constraints and improve the expressiveness of normalizing flow
architectures, Wu, Köhler, and Noé introduced stochastic normalizing flows which combine …

Inverse problems are ubiquitous in nature, arising in almost all areas of science and
engineering ranging from geophysics and climate science to astrophysics and …