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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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