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Enhanced temporal and spatial resolution in super‐resolution covariance imaging algorithm with deconvolution optimization
Author(s) -
Wang Xuehua,
Zhong Junping,
Wang Mingyi,
Xiong Honglian,
Han Dingan,
Zeng Yaguang,
He Haiying,
Tan Haishu
Publication year - 2021
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.202000292
Subject(s) - deconvolution , covariance , covariance function , algorithm , image resolution , resolution (logic) , point spread function , pixel , temporal resolution , mathematics , rational quadratic covariance function , covariance matrix , computer science , covariance intersection , artificial intelligence , optics , statistics , physics
Based on the numerical analysis that covariance exhibits superior statistical precision than cumulant and variance, a new SOFI algorithm by calculating the n orders covariance for each pixel is presented with an almost2 n ‐fold resolution improvement, which can be enhanced to 2 n via deconvolution. An optimized deconvolution is also proposed by calculating the ( n + 1) order SD associated with each n order covariance pixel, and introducing the results into the deconvolution as a damping factor to suppress noise generation. Moreover, a re‐deconvolution of the covariance image with the covariance‐equivalent point spread function is used to further increase the final resolution by above 2‐fold. Simulated and experimental results show that this algorithm can significantly increase the temporal–spatial resolution of SOFI, meanwhile, preserve the sample's structure. Thus, a resolution of 58 nm is achieved for 20 experimental images, and the corresponding acquisition time is 0.8 seconds.