Using a modified double deep image prior for crosstalk mitigation in multislice ptychography

Abstract

Multislice ptychography is a high-resolution microscopy technique used to image multiple separate axial planes using a single illumination direction. However, multislice ptychography reconstructions are often degraded by crosstalk, where some features on one plane erroneously contribute to the reconstructed image of another plane. Here, we demonstrate the use of a modified "double deep image prior" (DDIP) architecture in mitigating crosstalk artifacts in multislice ptychography. Utilizing the tendency of generative neural networks to produce natural images, a modified DDIP method yielded good results on experimental data. For one of the datasets, we show that using DDIP could remove the need of using additional experimental data, such as from x-ray fluorescence, to suppress the crosstalk. Our method may help x-ray multislice ptychography work for more general experimental scenarios.

arXiv comment10 pages, 5 figures

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Cited by in corpus (2)

• Noise-robust latent vector reconstruction in ptychography using deep generative models
• Fidelity-preserving enhancement of ptychography with foundational text-to-image models