  Modelling the impact of a material's mesostructure on device level
performance typically requires access to 3D image data containing all the
relevant information to define the geometry of the simulation domain. This
image data must include sufficient contrast between phases to distinguish each
material, be of high enough resolution to capture the key details, but also
have a large enough field-of-view to be representative of the material in
general. It is rarely possible to obtain data with all of these properties from
a single imaging technique. In this paper, we present a method for combining
information from pairs of distinct but complementary imaging techniques in
order to accurately reconstruct the desired multi-phase, high resolution,
representative, 3D images. Specifically, we use deep convolutional generative
adversarial networks to implement super-resolution, style transfer and
dimensionality expansion. To demonstrate the widespread applicability of this
tool, two pairs of datasets are used to validate the quality of the volumes
generated by fusing the information from paired imaging techniques. Three key
mesostructural metrics are calculated in each case to show the accuracy of this
method. Having confidence in the accuracy of our method, we then demonstrate
its power by applying to a real data pair from a lithium ion battery electrode,
where the required 3D high resolution image data is not available anywhere in
the literature. We believe this approach is superior to previously reported
statistical material reconstruction methods both in terms of its fidelity and
ease of use. Furthermore, much of the data required to train this algorithm
already exists in the literature, waiting to be combined. As such, our
open-access code could precipitate a step change by generating the hard to
obtain high quality image volumes necessary to simulate behaviour at the
mesoscale.
