Diffusion models have become increasingly popular for generative modeling due to their
ability to generate high-quality samples. This has unlocked exciting new possibilities for …

Calibrating land surface models and accurately quantifying their uncertainty are crucial for
improving the reliability of simulations of complex environmental processes. This, in turn …

In a great number of tasks in science and engineering, the goal is to infer an unknown image
from a small number of measurements collected from a known forward model describing …

Diffusion models excel in solving imaging inverse problems due to their ability to model
complex image priors. However, their reliance on large, clean datasets for training limits …

Deep neural networks trained as image denoisers are widely used as priors for solving
imaging inverse problems. While Gaussian denoising is thought sufficient for learning image …

Plug-and-Play (PnP) algorithms are a class of iterative algorithms that address image
inverse problems by combining a physical model and a deep neural network for …

We propose a solution for linear inverse problems based on higher-order Langevin
diffusion. More precisely, we propose pre-conditioned second-order and third-order …

We introduce multi-scale energy models to learn the prior distribution of images, which can
be used in inverse problems to derive the Maximum A Posteriori (MAP) estimate and to …

Score-based diffusion models (SDMs) offer a flexible approach to sample from the posterior
distribution in a variety of Bayesian inverse problems. In the literature, the prior score is …

Diffusion models have become a leading method for generative modeling of both image and
scientific data. As these models are costly to train and evaluate, reducing the inference cost …