A Parameter Refinement Method for Ptychography Based on Deep Learning Concepts

Condensed Matter

article

Sergio Carrato¹  Fulvio Billè¹  Alessandra Gianoncelli¹  George Kourousias¹  Francesco Guzzi² 

[1] Area Science Park;Image Processing Laboratory (IPL), Engineering and Architecture Department, University of Trieste

关键词: ptychography;    soft-X-ray microscopy;    X-ray measurements;    CDI;    phase retrieval;    automatic differentiation;    computational imaging;    parameter refining;    inverse problems;   

DOI  :  10.3390/condmat6040036

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

X-ray ptychography is an advanced computational microscopy technique, which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens, which can be used for high-precision X-ray measurements. However, coarse parametrisation in propagation distance, position errors and partial coherence frequently threaten the experimental viability. In this work, we formally introduce these actors, solving the whole reconstruction as an optimisation problem. A modern deep learning framework was used to autonomously correct 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, as well as on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.

【 授权许可】

CC BY