Efficient and flexible approach to ptychography using an optimization framework based on automatic differentiation

Abstract

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.

References in corpus (3)

• Exploiting speckle correlations to improve the resolution of wide-field fluorescence microscopy
• Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction
• Three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit

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• PtyRAD: A High-performance and Flexible Ptychographic Reconstruction Framework with Automatic Differentiation
• Noise-robust latent vector reconstruction in ptychography using deep generative models
• Sensor fusion in ptychography