Abstract
Coherent diffractive imaging (CDI), using both x-rays and electrons, has made extremely rapid progress over the past two decades. The associated reconstruction algorithms are typically iterative, and seeded with a crude first estimate. A deterministic method for Bragg Coherent Diffraction Imaging (Pavlov et al 2017 Sci. Rep. 7 1132) is used as a more refined starting point for a shrink-wrap iterative reconstruction procedure. The appropriate comparison with the autocorrelation function as a starting point is performed. Real-space and Fourier-space error metrics are used to analyse the convergence of the reconstruction procedure for noisy and noise-free simulated data. Our results suggest that the use of deterministic-CDI reconstructions, as a seed for subsequent iterative-CDI refinement, may boost the speed and degree of convergence compared to the cruder seeds that are currently commonly used. We also highlight the utility of monitoring multiple error metrics in the context of iterative refinement.
Highlights
August 2018Konstantin M Pavlov1,2,3 , Kaye S Morgan2,4,5 , Vasily I Punegov and David M Paganin
The ‘phase problem’ for propagating complex scalar fields seeks to reconstruct both their phase and amplitude given measurements of wave-field modulus [1]
In the present paper we show via x-ray simulations that such artefacts in the deterministic Bragg-Coherent diffractive imaging (CDI) reconstruction may be improved via subsequent iterative refinement
Summary
Konstantin M Pavlov1,2,3 , Kaye S Morgan2,4,5 , Vasily I Punegov and David M Paganin. Coherent diffractive imaging (CDI), using both x-rays and electrons, has made extremely rapid and DOI. A deterministic method for Bragg Coherent Diffraction. 7 1132) is used as a more refined starting point for a shrink-wrap iterative reconstruction procedure. Real-space and Fourier-space error metrics are used to analyse the convergence of the reconstruction procedure for noisy and noise-free simulated data. Our results suggest that the use of deterministic-CDI reconstructions, as a seed for subsequent iterative-CDI refinement, may boost the speed and degree of convergence compared to the cruder seeds that are currently commonly used. We highlight the utility of monitoring multiple error metrics in the context of iterative refinement
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