# A parameter refinement method for Ptychography based on Deep Learning concepts

arXiv: https://arxiv.org/abs/2105.08058 · HTML: https://mlsanity.com/paper/2105.08058/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 68 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Automatic differentiation in ML: Where we are and where we should be going](https://mlsanity.com/paper/1810.11530/) (2018)
- [Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation](https://mlsanity.com/paper/2012.12686/) (2020)
- [Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow](https://mlsanity.com/paper/1803.03434/) (2018)
- [Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction](https://mlsanity.com/paper/1902.03920/) (2019)
- [Efficient and flexible approach to ptychography using an optimization framework based on automatic differentiation](https://mlsanity.com/paper/2010.02074/) (2020)
- [Tutorials on X-ray Phase Contrast Imaging: Some Fundamentals and Some Conjectures on Future Developments](https://mlsanity.com/paper/1902.00364/) (2019)

## Built on by (most cited first, 4 of 4)

- [PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2310.07504/) (2023)
- [Compressive Ptychography using Deep Image and Generative Priors](https://mlsanity.com/paper/2205.02397/) (2022)
- [A modular software framework for the design and implementation of ptychography algorithms](https://mlsanity.com/paper/2205.04295/) (2022)
- [A Deep Generative Approach to Oversampling in Ptychography](https://mlsanity.com/paper/2207.14392/) (2022)

## Abstract (arXiv)

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.