论文标题
使用基于自动差异的优化框架，有效且灵活的Ptychography方法
Efficient and flexible approach to ptychography using an optimization framework based on automatic differentiation
论文作者
论文摘要
PtyChography是一种无透镜成像方法，可从一组衍射模式中获得波前传感和相敏感的显微镜。最近，已经表明，可以通过自动分化（AD）来实现PtyChography中的优化任务。在这里，我们提出了一个开放访问广告的开放式框架，该框架是通过受欢迎的机器学习库Tensorflow实现的。使用模拟，我们表明我们的基于广告的框架在重建速度和质量方面，与动量加速的Ptychographic迭代引擎（MPIE）的最新实现相当。基于广告的方法提供了极大的灵活性，正如我们通过将重建距离设置为可训练的参数所证明的那样。最后，我们在实验上证明我们的框架忠实地重建了生物标本。
Ptychography is a lensless imaging method that allows for wavefront sensing and phase-sensitive microscopy from a set of diffraction patterns. Recently, it has been shown that the optimization task in ptychography can be achieved via automatic differentiation (AD). Here, we propose an open-access AD-based framework implemented with TensorFlow, a popular machine learning library. Using simulations, we show that our AD-based framework performs comparably to a state-of-the-art implementation of the momentum-accelerated ptychographic iterative engine (mPIE) in terms of reconstruction speed and quality. AD-based approaches provide great flexibility, as we demonstrate by setting the reconstruction distance as a trainable parameter. Lastly, we experimentally demonstrate that our framework faithfully reconstructs a biological specimen.
