Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

With information and scale as central themes, this comprehensive survey explains how to
handle real problems in astronomical data analysis using a modern arsenal of powerful …

Compressive sensing (CS) has proved effective for tomographic reconstruction from
sparsely collected data or under-sampled measurements, which are practically important for …

The authors define ordered subset processing for standard algorithms (such as expectation
maximization, EM) for image restoration from projections. Ordered subsets methods group …

Fully updated throughout, with several new chapters, this second edition of Introduction to
Inverse Problems in Imaging guides advanced undergraduate and graduate students in …

Many image processing problems are ill-posed and must be regularized. Usually, a
roughness penalty is imposed on the solution. The difficulty is to avoid the smoothing of …

The expectation-maximization (EM) method can facilitate maximizing likelihood functions
that arise in statistical estimation problems. In the classical EM paradigm, one iteratively …

The modeling of spatial discontinuities for problems such as surface recovery, segmentation,
image reconstruction, and optical flow has been intensely studied in computer vision. While …

This introduction to the expectation–maximization (EM) algorithm provides an intuitive and
mathematically rigorous understanding of EM. Two of the most popular applications of EM …

The authors present a Markov random field model which allows realistic edge modeling
while providing stable maximum a posterior (MAP) solutions. The model, referred to as a …