Work

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

Year: 2019

Type: article

Abstract: 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.

Source: Applied Physics Letters

DOI: 10.1063/1.5125252

Authors: Leonhard Möckl, Petar N. Petrov, W. E. Moerner

Institution: Stanford University

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: National Institute of General Medical Sciences

Award: G4042233679