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 …

Sampling from diffusion models can be treated as solving the corresponding ordinary
differential equations (ODEs) with the aim of obtaining an accurate solution with as few …

The solution of inverse problems is of fundamental interest in medical and astronomical
imaging, geophysics as well as engineering and life sciences. Recent advances were made …

Deep learning has had tremendous success at learning low-dimensional representations of
high-dimensional data. This success would be impossible if there was no hidden low …

Diffusion models are a powerful class of generative models which simulate stochastic
differential equations (SDEs) to generate data from noise. While diffusion models have …

Matching objectives underpin the success of modern generative models and rely on
constructing conditional paths that transform a source distribution into a target distribution …

Conditional generative models became a very powerful tool to sample from Bayesian
inverse problem posteriors. It is well-known in classical Bayesian literature that posterior …

Score-based generative models (SGMs) sample from a target distribution by iteratively
transforming noise using the score function of the perturbed target. For any finite training set …

Diffusion and flow-matching models achieve remarkable generative performance but at the
cost of many sampling steps, this slows inference and limits applicability to time-critical …

Abstract Generative Models (GMs) have attracted considerable attention due to their
tremendous success in various domains, such as computer vision where they are capable to …