Accurate phase retrieval of complex 3D point spread functions with deep residual neural networks - Möckl, Leonhard; Petrov, Petar N.; Moerner, W. E. (Applied Physics Letters, 2019)
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.
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