期刊
APPLIED PHYSICS LETTERS
卷 115, 期 25, 页码 -出版社
AIP Publishing
DOI: 10.1063/1.5125252
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资金
- National Institute of General Medical Sciences [R35GM118067]
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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