A modular software framework for the design and implementation of ptychography algorithms
This provides a tool for researchers in computational microscopy to more efficiently develop and test algorithms, though it is incremental as it builds on existing methods.
The authors tackled the laborious process of designing and implementing new ptychography algorithms by developing SciComPty, a modular software framework that simulates datasets and tests reconstruction algorithms, leveraging GPU acceleration through PyTorch CUDA and releasing it as open-source.
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.