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Adorym:一个基于自动微分的多平台通用X射线图像重建框架。

Adorym: a multi-platform generic X-ray image reconstruction framework based on automatic differentiation.

作者信息

Du Ming, Kandel Saugat, Deng Junjing, Huang Xiaojing, Demortiere Arnaud, Nguyen Tuan Tu, Tucoulou Remi, De Andrade Vincent, Jin Qiaoling, Jacobsen Chris

出版信息

Opt Express. 2021 Mar 29;29(7):10000-10035. doi: 10.1364/OE.418296.

Abstract

We describe and demonstrate an optimization-based X-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement.

摘要

我们描述并展示了一个名为Adorym的基于优化的X射线图像重建框架。我们的框架提供了一个通用的正向模型,允许一个代码框架用于从近场全息术到飞扫叠层断层扫描等广泛的成像方法。通过使用自动微分进行优化,Adorym能够灵活地优化实验参数,包括探针位置、多个全息图对齐和物体倾斜。它的编写得到了对并行处理的强力支持,允许在高性能计算系统上处理大型数据集。我们在几个实验数据集上展示了它的应用,以通过参数优化来提高图像质量。

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