Observational analysis demonstrates advanced phase retrieval techniques for X-ray imaging, suggesting enhanced benchmarking for holographic and tomographic applications.
We present a Python toolbox for holographic and tomographic X-ray imaging. It comprises a collection of phase retrieval algorithms for the deeply holographic and direct contrast imaging regimes, including non-linear approaches and extended choices of regularization, constraint sets and optimizers, all implemented with a unified and intuitive interface. Moreover, it features auxiliary functions for (tomographic) alignment, image processing and simulation of imaging experiments. The capability of the toolbox is illustrated by an example of a catalytic nanoparticle, imaged in the deeply holographic regime at the `GINIX' instrument of the P10 beamline at the PETRA III storage ring (DESY, Hamburg, Germany). Due to its modular design, the toolbox can be used for algorithmic development and benchmarking in a lean and flexible manner, or be interfaced and integrated in the reconstruction pipeline of other synchrotron or X-ray free-electron laser instruments for phase imaging based on propagation.
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Lucht et al. (2025) studied this question.
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