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

Ming Du¹, Xiaojing Huang², Chris Jacobsen¹

¹Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA.

Journal of Synchrotron Radiation

|July 2, 2021

Summary

A new deep learning method using a modified double deep image prior (DDIP) effectively reduces crosstalk artifacts in multislice ptychography. This technique improves image quality without requiring extra experimental data, making X-ray microscopy more versatile.

Keywords:

artifact removal image processing multislice neural network ptychography

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Area of Science:

X-ray microscopy
Computational imaging
Deep learning applications

Background:

Multislice ptychography offers high-resolution imaging of multiple planes.
Crosstalk artifacts degrade reconstruction quality by mixing information between planes.

Purpose of the Study:

To mitigate crosstalk artifacts in multislice ptychography reconstructions.
To evaluate the efficacy of a modified double deep image prior (DDIP) architecture.

Main Methods:

Implementation of a modified double deep image prior (DDIP) neural network architecture.
Application of the DDIP method to experimental multislice ptychography data.

Main Results:

The modified DDIP successfully reduced crosstalk artifacts in reconstructed images.
DDIP eliminated the need for supplementary experimental data (e.g., X-ray fluorescence) for crosstalk suppression in specific cases.
The method demonstrated good performance on experimental datasets.

Conclusions:

Modified DDIP is an effective computational approach for improving multislice ptychography.
This method enhances image fidelity and broadens the applicability of X-ray multislice ptychography.
The DDIP technique offers a path towards more general experimental scenarios in X-ray microscopy.