In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence,
and it has been deployed in different fields of healthcare applications such as image …

Score-based diffusion models provide a powerful way to model images using the gradient of
the data distribution. Leveraging the learned score function as a prior, here we introduce a …

Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is
wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low …

GAN-based image restoration inverts the generative process to repair images corrupted by
known degradations. Existing unsupervised methods must carefully be tuned for each task …

High-resolution (HR) MRI scans obtained from research-grade medical centers provide
precise information about imaged tissues. However, routine clinical MRI scans are typically …

Meaningful uncertainty quantification in computer vision requires reasoning about semantic
information—say, the hair color of the person in a photo or the location of a car on the street …

This paper proposes a new methodology for performing Bayesian inference in imaging
inverse problems where the prior knowledge is available in the form of training data …

This paper provides an overview of current approaches for solving inverse problems in
imaging using variational methods and machine learning. A special focus lies on point …

Obtaining a useful estimate of an object from highly incomplete imaging measurements
remains a holy grail of imaging science. Deep learning methods have shown promise in …

Most deep learning models for computational imaging regress a single reconstructed image.
In practice, however, ill-posedness, nonlinearity, model mismatch, and noise often conspire …