A parameter refinement method for Ptychography based on Deep Learning concepts

TL;DR

This paper introduces a deep learning-based parameter refinement method for X-ray ptychography, enhancing reconstruction quality by autonomously correcting setup incoherences such as propagation distance errors and partial coherence.

Contribution

It presents a novel deep learning framework integrated into ptychography reconstruction to automatically correct setup incoherences, improving image quality and reliability.

Findings

Improved reconstruction quality on synthetic and real datasets.

Reduced setup incoherence effects through autonomous correction.

Open-source implementation in SciComPty software.

Abstract

X-ray Ptychography is an advanced computational microscopy technique which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens. However coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. In this work we formally introduced these actors, solving the whole reconstruction as an optimisation problem. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. Automatic procedures are indeed crucial to reduce the time for a reliable analysis, which has a significant impact on all the fields that use this kind of microscopy. We implemented our algorithm in our software framework, SciComPty, releasing it as open-source. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.