  Defects such as dislocations impact materials properties and their response
during external stimuli. Defect engineering has emerged as a possible route to
improving the performance of materials over a wide range of applications,
including batteries, solar cells, and semiconductors. Imaging these defects in
their native operating conditions to establish the structure-function
relationship and, ultimately, to improve performance has remained a
considerable challenge for both electron-based and x-ray-based imaging
techniques. However, the advent of Bragg coherent x-ray diffractive imaging
(BCDI) has made possible the 3D imaging of multiple dislocations in
nanoparticles ranging in size from 100 nm to1000 nm. While the imaging process
succeeds in many cases, nuances in identifying the dislocations has left manual
identification as the preferred method. Derivative-based methods are also used,
but they can be inaccurate and are computationally inefficient. Here we
demonstrate a derivative-free method that is both more accurate and more
computationally efficient than either derivative- or human-based methods for
identifying 3D dislocation lines in nanocrystal images produced by BCDI. We
formulate the problem as a min-max optimization problem and show exceptional
accuracy for experimental images. We demonstrate a 260x speedup for a typical
experimental dataset with higher accuracy over current methods. We discuss the
possibility of using this algorithm as part of a sparsity-based phase retrieval
process. We also provide the MATLAB code for use by other researchers.
