Engineering materials are heterogeneous mixtures at the microscale. Often the properties of the material have a strong dependence on features which have been obscured by phase transformations. Reconstruction of the prior structure is ill-posed, as the forward transformation is one-to-many. Here we present a graph partitioning approach to incorporate physically motivated priors in stochastic image reconstruction. The approach will be demonstrated on recovery of pre-transformation microstructures as well as segmentation and clustering in materials microscopy.

This presentation is part of Minisymposium “MS11 - Computational Imaging for Micro- and Nano-structures in Materials Science (2 parts)”
organized by: Brendt Wohlberg (Los Alamos National Laboratory) , Jeff Simmons (Air Force Research Laboratory) .

Authors:

Stephen Niezgoda (The Ohio State University)

Alexander Brust (The Ohio State University)

Eric Payton (Air Force Research Laboratory)

Keywords:

bayesian methods, electron diffraction, electron microscopy, image reconstruction, image segmentation, inverse problems, statistical inverse estimation methods