Open-3DSIM is an open-source reconstruction platform for three-dimensional structured illumination microscopy. We demonstrate its superior performance for artifact suppression and high-fidelity reconstruction relative to other algorithms on various specimens and over a range of signal-to-noise levels. Open-3DSIM also offers the capacity to extract dipole orientation, paving a new avenue for interpreting subcellular structures in six dimensions (xyzθλt). The platform is available as MATLAB code, a Fiji plugin and an Exe application to maximize user-friendliness.
With optical section and defocus removal effect, three-dimensional structured illumination microscopy (3DSIM) can get a whole sight of intracellular organelle. Here, Open-3DSIM is reported as an open-source reconstruction platform with double improvement on lateral and axial resolution. MATLAB code, ImageJ version and Exe application are provided for biologists and engineers to maximize its user-friendliness and prompt its further development. Through adaptive parameter estimation and spectrum filter optimization, we demonstrate its superior performance of artifact suppression and defocus elimination over other algorithms on various specimens, under gradient signal-to-noise levels. Moreover, with the capacity to extract the dipole orientation, Open-3DSIM paves a new avenue for interpreting the subcellular structures in six dimensions.
We demonstrate numerically a novel repeater-based three-stage cascaded soliton self-frequency shift (SSFS) structure including a germania-core fiber, an Er 3+ ZBLAN fiber amplifier, and an indium fluoride (InF3) or TeO2-Bi2O3-ZnO-Na2O (TBZN) fiber. Wide wavelength tunability of 2-4.4 µm and 2-5.0 µm was achieved with a 5 m-long InF3 and a 0.2 m-long TBZN fiber, respectively. Numerical results show that with the same input pulses, stronger SSFS effect with longer wavelength edge occurred in TBZN fiber with shorter fiber length, while Raman solitons with higher energy, conversion efficiency and shorter pulse duration were generated in InF3 fiber. Compared with the commonly used single frequency shift structure, both the energy and conversion efficiency of Raman solitons in the designed repeater-based threestage cascaded structure were significantly higher when the same tunable range was achieved. Our work could provide an efficient way to simultaneously improve the tunability, output energy, and conversion efficiency of the existing all-fiber laser sources with a lower operation energy.
Principal component analysis (PCA), a common dimensionality reduction method, is introduced into SIM to identify the frequency vectors and pattern phases of the illumination pattern with precise subpixel accuracy, fast speed, and noise-robustness, which is promising for real-time and long-term live-cell imaging.
Despite the grand advances in fluorescence microscopy, the photon budget of fluorescent molecules remains the fundamental limiting factor for major imaging parameters, such as temporal resolution, duration, contrast, and even spatial resolution. Computational methods can strategically utilize the fluorescence photons against the imaging noise, to break the abovementioned limits. Here, we propose a multi-resolution analysis (MRA) approach to recharacterize and extract the two main characteristics of fluorescence images: (1) high contrast across the edge, and (2) high continuity along the edge. By regularizing the solution using framelet and curvelet domain sparsity, we develop MRA deconvolution algorithm for fluorescence image, which allows fine detail recovery even with negative signal-to-noise-ratio (SNR), and can provide more than 2-fold physical resolution enhancement with conspicuously fewer artifacts than maximum likelihood estimation (MLE) methods. Furthermore, we develop DeepMRA deconvolution algorithm that can provide computational background inhibition through a bias thresholding mechanism while deconvolving a fluorescence image. Compared with conventional background mitigation schemes, this novel deconvolution canonical form can deal with severer background and better preserve the high-frequency and low-intensity details, which are commonly disrupted by other algorithms. We demonstrate that the MRA and DeepMRA deconvolution algorithms can improve the SNR and resolution of biological images in various microscopies, such as wide-field, confocal, spinning-disk confocal (SD-confocal), light-sheet, structured illumination microscopy (SIM), and stimulated excitation depletion (STED) microscopy.
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