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arXiv · 1906.01748

Accurate phase retrieval of complex point spread functions with deep residual neural networks

Leonhard MÖckl·Petar N. Petrov·W. E. Moerner
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Abstract

Phase retrieval, i.e. the reconstruction of phase information from intensity information, is a central problem in many optical systems. Here, we demonstrate that a deep residual neural net is able to quickly and accurately perform this task for arbitrary point spread functions (PSFs) formed by Zernike-type phase modulations. Five slices of the 3D PSF at different focal positions within a two micron range around the focus are sufficient to retrieve the first six orders of Zernike coefficients.

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Leonhard MÖckl, Petar N. Petrov, W. E. Moerner. 2019-06-04. Accurate phase retrieval of complex point spread functions with deep residual neural networks. https://doi.org/10.1063/1.5125252

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