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.

翻译：X射线外观学是一种先进的计算显微镜技术,它正在提供生物和纳米技术样本的非常详细的定量成像,尽管在传播距离、位置错误和部分一致性方面粗略的相配往往威胁到实验的可行性。在这项工作中,我们正式引进了这些行为者,解决了整个重建的优化问题。现代深层学习框架被用来自主地纠正设置不协调的问题,从而改进了结构学重建的质量。自动程序对于缩短进行可靠分析的时间确实至关重要,因为这种分析对使用这种显微镜的所有领域都具有重大影响。我们在软件框架中应用了我们的算法,SciComPty,作为开放源释放了它。我们在合成数据集和Elettra同步器设施双光谱光谱上获取的真实数据上测试了我们的系统。