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Defects such as dislocations impact materials properties and their responseduring external stimuli. Defect engineering has emerged as a possible route toimproving the performance of materials over a wide range of applications,including batteries, solar cells, and semiconductors. Imaging these defects intheir native operating conditions to establish the structure-functionrelationship and, ultimately, to improve performance has remained aconsiderable challenge for both electron-based and x-ray-based imagingtechniques. However, the advent of Bragg coherent x-ray diffractive imaging(BCDI) has made possible the 3D imaging of multiple dislocations innanoparticles ranging in size from 100 nm to1000 nm. While the imaging processsucceeds in many cases, nuances in identifying the dislocations has left manualidentification as the preferred method. Derivative-based methods are also used,but they can be inaccurate and are computationally inefficient. Here wedemonstrate a derivative-free method that is both more accurate and morecomputationally efficient than either derivative- or human-based methods foridentifying 3D dislocation lines in nanocrystal images produced by BCDI. Weformulate the problem as a min-max optimization problem and show exceptionalaccuracy for experimental images. We demonstrate a 260x speedup for a typicalexperimental dataset with higher accuracy over current methods. We discuss thepossibility of using this algorithm as part of a sparsity-based phase retrievalprocess. We also provide the MATLAB code for use by other researchers.

Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation