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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) .