A parameter refinement method for Ptychography based on Deep Learning concepts
This work addresses parameter refinement for ptychography, an incremental improvement that reduces analysis time for fields using this microscopy technique.
The paper tackled the problem of coarse parametrisation in X-ray ptychography, which threatens experiment viability, by using a deep learning framework to autonomously correct setup incoherences, improving reconstruction quality as tested on synthetic and real data from the TwinMic beamline.
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.