# A modular software framework for the design and implementation of ptychography algorithms

arXiv: https://arxiv.org/abs/2205.04295 · HTML: https://mlsanity.com/paper/2205.04295/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [SHARP: a distributed, GPU-based ptychographic solver](https://mlsanity.com/paper/1602.01448/) (2016)
- [Alternating Projection, Ptychographic Imaging and Phase Synchronization](https://mlsanity.com/paper/1402.0550/) (2014)
- [PyNX: high performance computing toolkit for coherent X-ray imaging based on operators](https://mlsanity.com/paper/2008.11511/) (2020)
- [A parameter refinement method for Ptychography based on Deep Learning concepts](https://mlsanity.com/paper/2105.08058/) (2021)

## Built on by (most cited first, 1 of 1)

- [Position-Blind Ptychography: Viability of image reconstruction via data-driven variational inference](https://mlsanity.com/paper/2509.25269/) (2025)

## Abstract (arXiv)

Computational methods are driving high impact microscopy techniques such as ptychography. However, the design and implementation of new algorithms is often a laborious process, as many parts of the code are written in close-to-the-hardware programming constructs to speed up the reconstruction. In this paper, we present SciComPty, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art and new reconstruction algorithms. Despite its simplicity, the software leverages GPU accelerated processing through the PyTorch CUDA interface. This is essential to design new methods that can readily be employed. As an example, we present an improved position refinement method based on Adam and a new version of the rPIE algorithm, adapted for partial coherence setups. Results are shown on both synthetic and real datasets. The software is released as open-source.