AuthorsMikhalychev, Alexander B.
Karuseichyk, Ilya L.
Vlasenko, Svetlana V.
Michels, Dominik L.
Mogilevtsev, Dmitri S.
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/673053
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AbstractQuantum imaging emerged quite recently as a breakthrough technology of overcoming the diffraction limit in microscopy and enhancement of optical resolution without the necessity to use hard radiation or perform scanning in the near field  , . Both, the quantum imaging itself and the more 'classical' techniques inspired by it (for example, super-resolution optical fluctuations imaging - SOFI  ), rely on detection and analysis of photon (intensity) correlations. Typically, it is believed that the more correlated illuminating light is used and the higher order of the correlations is measured, the larger super-resolution can be achieved. That conclusion is based on efficient narrowing of the point-spread function, which, however, does not necessarily imply better resolution as the ability to reconstruct smaller features of the investigated object successfully.
CitationMikhalychev, A. B., Karuseichyk, I. L., Vlasenko, S. V., Bessire, B., Lyakhov, D. A., Michels, D. L., … Mogilevtsev, D. S. (2021). Information Analysis for Quantum Imaging Optimization. 2021 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC). doi:10.1109/cleo/europe-eqec52157.2021.9541952
Conference/Event name2021 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2021