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Portable deep learning singlet microscope
Author(s) -
Shen Hua,
Gao Jinming
Publication year - 2020
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.202000013
Subject(s) - microscope , lens (geology) , optics , microscopy , singlet state , computer science , monochromatic color , signal (programming language) , materials science , artificial intelligence , physics , nuclear physics , excited state , programming language
Having the least lenses, the significant feature of the singlet imaging system, helps the development of the portable and cost‐effective microscopes. A novel method of monochromatic/color singlet microscopy, which is combined with only one aspheric lens and deep learning computational imaging technology, is proposed in this article. The designed singlet aspheric lens is an approximate linear signal system, which means modulation‐transfer‐function curves on all field‐of‐views (5 mm diagonally) are almost coincident with each other. The purpose of the designed linear signal system is to further improve the resolution of our microscope by using deep learning algorithm. As a proof of concept, we designed a singlet microscopy based on our method, which weighs only 400 g. The experimental data and results of the sample USAF−1951 target and bio‐sample (the Equisetum‐arvense Strobile L.S), prove that the performance of the proposed singlet microscope is competitive to a commercial microscope with the 4X/NA0.1 objective lens. We believe that our idea and method would guide to design more cost‐effective and powerful singlet imaging system.