Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling
detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its …

Score-based generative models have demonstrated highly promising results for medical
image reconstruction tasks in magnetic resonance imaging or computed tomography …

Variational regularisation is the primary method for solving inverse problems, and recently
there has been considerable work leveraging deeply learned regularisation for enhanced …

We propose a framework to perform Bayesian inference using conditional score-based
diffusion models to solve a class of inverse problems in mechanics involving the inference of …

Generative models have gained popularity for their potential applications in imaging
science, such as image reconstruction, posterior sampling and data sharing. Flow-based …

An emerging new paradigm for solving inverse problems is via the use of deep learning to
learn a regularizer from data. This leads to high-quality results, but often at the cost of …

Computed tomography (CT) is a widely used non-invasive diagnostic method in various
fields, and recent advances in deep learning have led to significant progress in CT image …

Traffic matrices (TMs) contain crucial information for managing networks, optimizing traffic
flow, and detecting anomalies. However, directly measuring traffic to construct a TM is …

We propose to sample from high-dimensional posterior distributions arising in physics-
based inverse problems using conditional score-based generative models. The proposed …

Seismic images often contain both coherent and random artifacts which complicate their
interpretation. To mitigate these artifacts, we introduce a novel unsupervised deep-learning …