In recent years, deep learning has achieved remarkable empirical success for image
reconstruction. This has catalyzed an ongoing quest for the precise characterization of the …

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 …

Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems,
leading to edge divergence with degraded image quality. Recently, deep learning has been …

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 …

Abstract Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative
proximal algorithms by replacing a proximal operator by a denoising operation. When …

We investigate the parameterization of deep neural networks that by design satisfy the
continuity equation, a fundamental conservation law. This is enabled by the observation that …

Plug-and-play algorithms constitute a popular framework for solving inverse imaging
problems that rely on the implicit definition of an image prior via a denoiser. These …

The emergence of deep-learning-based methods to solve image-reconstruction problems
has enabled a significant increase in quality. Unfortunately, these new methods often lack …

We propose to learn non-convex regularizers with a prescribed upper bound on their weak-
convexity modulus. Such regularizers give rise to variational denoisers that minimize a …

Using diffusion models to solve inverse problems is a growing field of research. Current
methods assume the degradation to be known and provide impressive results in terms of …