2017/02/16 by Eduardo X. Miqueles, Nathaly Lopes Archilha, Miqueles, Eduardo X. +7 · 1 citation
Materials Science · Mathematics · Physics and Astronomy · #Advanced X-ray Imaging Techniques #Artificial intelligence #Computer science #Electron and X-Ray Spectroscopy Techniques #FOS: Mathematics #Mathematical analysis #Mathematics #Numerical Analysis (math.NA) #Phase retrieval #Regularization (linguistics) #X-ray Diffraction in Crystallography
paper · pdf · doi:10.48550/arxiv.1702.05092
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2017/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
It was recently shown that the phase retrieval imaging of a sample can be modeled as a simple convolution process. Sometimes, such a convolution depends on physical parameters of the sample which are difficult to estimate a priori. In this case, a blind choice for those parameters usually lead to wrong results, e.g., in posterior image segmentation processing. In this manuscript, we propose a simple connection between phase-retrieval algorithms and optimization strategies, which lead us to ways of numerically determining the physical parameters