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.
Structured illumination microscopy (SIM) is a powerful super-resolution method in bioscience, featuring full-field imaging and high photon efficiency. However, artifact-free super-resolution image reconstruction requires precise knowledge about the illumination parameters. In this work, we propose an efficient and robust SIM algorithm based on principal component analysis (PCA-SIM) combines iteration-free reconstruction, noise robustness, and limited computational complexity. These characteristics make PCA-SIM a promising method for high-speed, long-term, artifact-free super-resolution imaging of live cells.
Structured Illumination Microscopy(SIM) is a suitable instrument for fluorescence imaging, especially dynamic imaging of live cells. It has two significant advantages of fast imaging speed and low excitation light energy density, and can reach the resolution of 100nm. In this article, we focus on the traditional reconstruction algorithm of SIM, which separates the spectrum with 9 raw images of 3-step phase-shifted picture in 3 orientations and operates the reconstruction with the correct displacement in Fourier space. However, without considering the initial phase error and the possibility that the displacement is a sub-pixel, there are artifacts in the results of the traditional reconstruction algorithm of SIM. To eliminate the artifacts and improve the imaging quality, we analyze the causes of various artifacts, and study the cross-correlation-based reconstruction algorithm of SIM, using the maximum cross-correlation value between the spectrum to get the correct displacement. Then, we simulate the illumination experimental parameters on the images of the reconstructed results. From the perspective of both hardware and software, we respectively consider the construction of the home-built SIM setup and the reconstruction software design, and finally realize the tri-color SIM system.
KEYWORDS: Phase shifts, Microscopy, Real time imaging, Image processing, Super resolution, Optical transfer functions, Modulation, Microscopes, Luminescence, Double patterning technology
KEYWORDS: Super resolution, Reconstruction algorithms, Phase shift keying, Signal to noise ratio, Modulation, Principal component analysis, Microscopy, Fourier transforms, Optical transfer functions, Image resolution
Structured illumination microscopy (SIM) is a widely available super-resolution technique for bioscience, especially for living cell research, due to its high photon efficiency. However, the quality of SIM depends extremely on the post-processing algorithms (parameter estimation and image reconstruction), where parameter estimation is the critical guarantee for successful super-resolution reconstruction. In this letter, we present a novel SIM approach based on principal component analysis (PCA-SIM) that statistically purifies experimental parameters from noise contamination to achieve high-definition super-resolution reconstruction. Experiments demonstrate that our method achieves more accurate (0.01 pixel wave vector and 0.1% of 2π initial phase) parameter estimation and superior noise immunity with an order of magnitude higher efficiency than conventional cross-correlation-based methods, offering the possibility of faster, less photon dose, longer duration living cell SIM.
Among the popular fluorescence super-resolution microscopy imaging technologies that had broken the optical diffraction limit, structured illumination microscopy (SIM) holds the advantages of low phototoxicity, weak photobleaching, and fast imaging speed, and it is currently one of the mainstream technologies for super-resolution microscopy imaging of living cells. SIM uses the modulation of the structured illumination patterns to encode highfrequency information in the raw images into the low-frequency region, allowing it to pass through the optical transfer function (OTF), and then obtains super-resolution images through demodulation and reconstruction. The reconstructed image is affected by some important parameters of the illumination light field, so it is necessary to accurately estimate the unknown parameters of the illumination light field, especially the initial phase, to minimize artifacts in the reconstructed image. In this work, we have completed the experimental operation of SIM, and image reconstruction based on different phase reconstruction algorithms. Firstly, we reviewed the development history of SIM, and systematically introduced the principle of SIM to achieve super-resolution imaging and the phase estimation algorithms. Then, we discussed the technical difficulties of the hardware setup, and built a dual-beam interference SIM system based on the ferroelectric liquid crystal spatial light modulator (FLC-SLM). Finally, we used different phase estimation algorithms to extract the initial phases of the collected images, and some comparable results are obtained.
With the development of various fluorescence technologies and optical control, fluorescence super-resolution microscopy has broken the limit of optical diffraction. Among them, the structural illumination microscopy (SIM), which combines structured light illumination and wide-field fluorescence imaging, uses structural illumination to mix in Fourier space to bring high-frequency information into the passband of the optical transfer function (OTF) to achieve super-resolution imaging. And having the advantages of weak phototoxicity and photobleaching, and fast imaging speed, SIM is currently one of the most mainstream techniques for super-resolution microscopy imaging of living cells. In this work, we have completed the theoretical simulation of SIM and the experimental operation. Firstly, we review the development of SIM, and systematically introduces its super-resolution imaging principle. Then, we discuss the technical difficulties of the hardware part, and builds a set of dual-beam interferometric SIM based on ferroelectric liquid crystal spatial light modulator, achieving that it only takes 270ms to collect 9 original images, and modulates the polarization characteristics of the illumination light to improve the interference fringe contrast and energy utilization. Finally, by using the open-source plugin Hifi-SIM to achieve image reconstruction, we obtain some ideal results.
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