Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

Tripathi, Ashish; McNulty, Ian; Munson, Todd; Wild, Stefan M.

doi:10.1364/oe.24.024719

Cited by 4 publications

(3 citation statements)

References 41 publications

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“…This speedup makes incorporating dislocation identification into existing phase retrieval algorithms feasible. It could be used to perform the transformation to the dislocation basis, which tends to be sparse [24]. Sparsity using the dislocation basis could be used to circumvent traditional constraints in BCDI, ptychography, and other methods that rely on phase retrieval.…”

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confidence: 99%

“…While these studies have shown great promise, the breadth of 3D BCDI dislocation dynamics measurements and techniques could expand substantially if accurate, robust, and rapid methods existed to determine the 3D dislocation line structure. For example, the datasets generated at diffraction-limited storage rings will likely be too large for existing derivative-based and human-in-the-loop-based methods [23], and the "dislocation basis" could potentially be used as a sparse basis to circumvent constraints in phase retrieval [24]. Here we present such a method by reformulating the dislocation core identification problem as a min-max optimization problem that can be solved rapidly.…”

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

2018

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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 derivativeor 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.

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

2018

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“…It has been observed that the ER/HIO/Shrinkwrap combination can aggressively shrink the crystal volume and become trapped in a local minimum that prevents convergence to the true solution 17 . This is because Shrinkwrap is effectively a sparsity promoting operation 44 . The Max Volume metric is designed to favor “anti-sparsity” directly by choosing the largest crystal, which is defined as the crystal with the most nonzero pixels after thresholding the density at 20% of its maximum.…”

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confidence: 99%

Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging

Ulvestad

Nashed

Beutier

et al. 2017

Sci Rep

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Crystallographic defects such as dislocations can significantly alter material properties and functionality. However, imaging these imperfections during operation remains challenging due to the short length scales involved and the reactive environments of interest. Bragg coherent diffractive imaging (BCDI) has emerged as a powerful tool capable of identifying dislocations, twin domains, and other defects in 3D detail with nanometer spatial resolution within nanocrystals and grains in reactive environments. However, BCDI relies on phase retrieval algorithms that can fail to accurately reconstruct the defect network. Here, we use numerical simulations to explore different guided phase retrieval algorithms for imaging defective crystals using BCDI. We explore different defect types, defect densities, Bragg peaks, and guided algorithm fitness metrics as a function of signal-to-noise ratio. Based on these results, we offer a general prescription for phasing of defective crystals with no a priori knowledge.

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An algorithm of fuzzy edge detection for wetland remote sensing image based on fuzzy theory

Wang

2022

Appl Nanosci

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Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

Cited by 4 publications

References 41 publications

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

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

Identifying Defects with Guided Algorithms in Bragg Coherent Diffractive Imaging

An algorithm of fuzzy edge detection for wetland remote sensing image based on fuzzy theory

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