论文标题
AdoryM：基于自动差异化的多平台通用X射线图像重建框架
Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation
论文作者
论文摘要
我们描述并演示了一个基于优化的X射线图像重建框架，称为AdoryM。我们的框架提供了一个通用的远期模型，可以将一个代码框架用于从近场全息图到蝇can Pychographichogichy层析成像等广泛的成像方法。通过使用自动分化进行优化，AdoryM具有完善实验参数（包括探针位置，多个全息图比对和对象倾斜）的灵活性。它的书面作用有很大的支持，对并行处理，从而可以在高性能计算系统上处理大型数据集。我们证明了它在几个实验数据集上的使用，以通过参数细化来显示改进的图像质量。
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 and 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.
