Accurate phase retrieval of complex 3D point spread functions with deep residual neural networks
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.
- Publication:
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Applied Physics Letters
- Pub Date:
- December 2019
- DOI:
- 10.1063/1.5125252
- arXiv:
- arXiv:1906.01748
- Bibcode:
- 2019ApPhL.115y1106M
- Keywords:
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- Electrical Engineering and Systems Science - Image and Video Processing;
- Physics - Optics
- E-Print:
- 8 pages, 4 figures