Enhanced temporal and spatial resolution in super-resolution covariance imaging algorithm with deconvolution optimization |
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Authors: | Xuehua Wang Junping Zhong Mingyi Wang Honglian Xiong Dingan Han Yaguang Zeng Haiying He Haishu Tan |
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Affiliation: | 1. School of Physics and Optoelectronic Engineering, Foshan University, Foshan, Guang dong, China;2. School of Materials Science and Energy Engineering, Foshan University, Foshan, Guang dong, China |
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Abstract: | 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 almost -fold resolution improvement, which can be enhanced to 2n 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. |
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Keywords: | algorithm deconvolution fast imaging fluorescence microscopy super-resolution] |
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