What is it about?
The novel methodology presented herre using image synthesis ideas and newly developed priors on sparsity, within a very high-dimensional MCMC-based inferential scheme.
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Why is it important?
We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a Scanning Electron Microscope. The method is applied to learn the material density of a 3-D sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.
Perspectives
Working on this article with mathematicians has given me different perspective of solving a problem using both mathematical and computation tools. Designing the experiment was a real challenge.
Professor Shashi Paul
De Montfort University
Read the Original
This page is a summary of: Bayesian Estimation of Density via Multiple Sequential Inversions of Two-Dimensional Images With Application to Electron Microscopy, Technometrics, April 2015, Taylor & Francis,
DOI: 10.1080/00401706.2014.923789.
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