Ms. Yuxia Huang Profile

Yuxia Huang

at Nanjing Univ of Science and Technology

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Proceedings Article | 27 November 2023 Poster + Paper

Structured illumination microscopy based on the parameter estimation of the bisection method

Kailong Xu, Jiaming Qian, Shijie Feng, Yuxia Huang, Weiyi Xia, Ying Bi

Proceedings Volume 12766, 1276613 (2023) https://doi.org/10.1117/12.2689704

KEYWORDS: Reconstruction algorithms, Super resolution, Microscopy, Structured light, Optical imaging

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Structured illumination microscopy (SIM) stands out among full-field super-resolution imaging modes in life sciences because of its high imaging speed, low phototoxicity, and low photobleaching. Traditional SIM technology requires accurate illumination parameters of 9 original images to achieve artifact-free super-resolution image reconstruction. Currently, the most popular algorithm with excellent parameter estimation performance is the two-dimensional cross-correlation algorithm, which is implemented by a large number of cross-correlation calculations in each direction. However, this computationally intensive algorithm isn’t a better choice for the technical application of real-time and long-term live cell imaging. In this work, on the premise of ensuring the accuracy of parameter estimation and noise resistance, we propose a bisection-based parameter estimation algorithm that can reduce the number of cross-correlation calculations in each direction by an order of magnitude. In the algorithm, the whole pixel position of the wave vector is first determined. Then the cross-correlation value at both ends of the XY direction is calculated, and the larger cross-correlation value position and the middle position are taken as the position for the next cross-correlation value calculation, so as to gradually approach the actual wave vector position from coarse to fine. To verify the proposed algorithm, super-resolution image reconstruction for fluorescent samples was performed. The experimental results show that compared with traditional SIM algorithms, the proposed parameter estimation algorithm is more accurate and anti-noise, and less computationally intensive (with only about 1/10 of the original cross-correlation value), which is highly significant for the technical application of real-time and long-term live cell imaging.

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