Accurate phase retrieval of complex 3D point spread functions with deep residual neural networks

Phase retrieval, i.e., the reconstruction of phase information from intensity information, is a central problem in many optical systems. Imaging the emission from a point source such as a single molecule is one example. Here, we demonstrate that a deep residual neural net is able to quickly and accu...

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Bibliographic Details
Published in:Applied physics letters 2019-12, Vol.115 (25), p.251106-251106
Main Authors: Möckl, Leonhard, Petrov, Petar N., Moerner, W. E.
Format: Article
Language:English
Subjects:
Applied physics
Artificial neural networks
Image reconstruction
Phase retrieval
Photonics and Optoelectronics
Point sources
Point spread functions
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Summary:Phase retrieval, i.e., the reconstruction of phase information from intensity information, is a central problem in many optical systems. Imaging the emission from a point source such as a single molecule is one example. Here, we demonstrate that a deep residual neural net is able to quickly and accurately extract the hidden phase for general point spread functions (PSFs) formed by Zernike-type phase modulations. Five slices of the 3D PSF at different focal positions within a two micrometer range around the focus are sufficient to retrieve the first six orders of Zernike coefficients.
ISSN:0003-6951
1077-3118
DOI:10.1063/1.5125252