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Related Experiment Video

Updated: Jun 10, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Noniterative approach to the missing data problem in coherent diffraction imaging by phase retrieval.

Nobuharu Nakajima¹

¹Faculty of Engineering, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, 432-8561, Japan.

|July 22, 2010

Summary

This summary is machine-generated.

A novel noniterative method reconstructs objects from coherent x-ray diffraction imaging data lacking central intensities. This approach overcomes limitations of iterative methods for phase retrieval with missing beam-stop data.

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Area of Science:

Coherent x-ray diffraction imaging
Phase retrieval

Background:

Intense beams in coherent x-ray diffraction imaging (CXDI) necessitate beam stops, which block central diffraction data.
Existing iterative phase-retrieval methods struggle with reconstructing objects from data deficient in central intensities.

Purpose of the Study:

To present a noniterative alternative for object reconstruction in CXDI when central diffraction data is missing.
To demonstrate the efficacy of this new method using computer simulations.

Main Methods:

A noniterative phase-retrieval method is proposed, combining an interpolation technique based on the sampling theorem with a previously developed aperture-array filter.
The method addresses the deficiency of central data caused by beam stops in CXDI.

Main Results:

Computer simulations successfully reconstructed a complex-amplitude object from a single diffraction pattern with a missing central data area.
The noniterative approach effectively handles missing data where iterative methods, relying on nonnegativity constraints, often fail.

Conclusions:

The developed noniterative method offers a viable alternative for object reconstruction in CXDI with missing central data.
This technique expands the applicability of phase retrieval in situations where iterative methods are limited by data deficiencies.