Structured illumination microscopy (SIM), with the advantages of full-field imaging and low photo-damage, is one of the most well-established fluorescence super-resolution microscopy techniques that raised great interest in biological sciences. However, conventional SIM techniques generally require at least nine images for image reconstruction, and the quality of super-resolution significantly depends on high-accuracy illumination parameter estimation, which is usually computationally intense and time-consuming. To address these issues, we propose a robust seven-frame SIM reconstruction algorithm with accelerated correlation-enabled parameter estimation. First, a modulation-assigned spatial filter is employed to remove unreliable backgrounds associated with low signal-to-noise ratios. Then, we propose a coarse-to-fine accelerated correlation algorithm to eliminate the redundant iterations of the traditional correlation-based scheme. The frame reduction is achieved by a specially designed phase-shifting strategy combined with pixel-wise fluorescence pre-calibration. We experimentally demonstrate that, compared with conventional iterative correlation-based methods, the proposed algorithm improves the computational efficiency by a factor of 4.5 while maintaining high accuracy illumination parameter estimation. Meanwhile, our method achieves high-quality super-resolution reconstruction even with a reduction in two raw images, which improves the efficiency of image acquisition and ensures the robustness toward complex experimental environments.
Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation
Jiaming Qian, Yu Cao, Kailong Xu, Ying Bi, Weiyi Xia, Qian Chen, Chao Zuo; Robust frame-reduced structured illumination microscopy with accelerated correlation-enabled parameter estimation. Appl. Phys. Lett. 10 October 2022; 121 (15): 153701. https://doi.org/10.1063/5.0107510
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