Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

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 …

Uncertainty quantification is crucial for the deployment of image restoration models in safety-
critical domains, like autonomous driving and biological imaging. To date, methods for …

Diffusion models have achieved tremendous success in generating high-dimensional data
like images, videos and audio. These models provide powerful data priors that can solve …

With the onset of diffusion-based generative models and their ability to generate text-
conditioned images, content generation has received a massive invigoration. Recently …

We propose a new method for solving imaging inverse problems using text-to-image latent
diffusion models as general priors. Existing methods using latent diffusion models for …

In recent years, most exciting inputs (MEIs) synthesized from encoding models of neuronal
activity have become an established method for studying tuning properties of biological and …

Diffusion models have achieved remarkable success in image and video generation. In this
work, we demonstrate that diffusion models can also\textit {generate high-performing neural …

Blind face restoration is an important task in computer vision and has gained significant
attention due to its wide-range applications. Previous works mainly exploit facial priors to …

Diffusion generative models unlock new possibilities for inverse problems as they allow for
the incorporation of strong empirical priors into the process of scientific inference. Recently …