School of Physics and Optoelectronic Engineering, Foshan University, Foshan, Guang dong, China.
School of Materials Science and Energy Engineering, Foshan University, Foshan, Guang dong, China.
J Biophotonics. 2021 Feb;14(2):e202000292. doi: 10.1002/jbio.202000292. Epub 2020 Nov 16.
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 2 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.
基于协方差比累积量和方差具有更高统计精度的数值分析，本文提出了一种新的 SOFI 算法，通过计算每个像素的 n 阶协方差，分辨率提高了近 1 倍，通过去卷积可进一步提高到 2 倍。通过计算与每个 n 阶协方差像素相关的(n+1)阶 SD，并将结果作为阻尼因子引入去卷积中以抑制噪声产生，本文还提出了一种优化的去卷积方法。此外，使用协方差等效点扩散函数对协方差图像进行重新去卷积，可使最终分辨率进一步提高 2 倍以上。模拟和实验结果表明，该算法可显著提高 SOFI 的时空分辨率，同时保持样本的结构。因此，在 20 张实验图像中实现了 58nm 的分辨率，相应的采集时间为 0.8 秒。
