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Simulation results.: (a) The correlation graph between the reconstructed image and the original one as a function of the iteration number. (b) is the image that was generated by the FSP of the reconstructed field for a distance of 4 μm and (c) is the originally generated image at distance of 4 μm. Both images are presented with 40% increased brightness and contrast and were pseudo-colored for their better visualization.
Source publication
Optical sectioning microscopy can provide highly detailed three dimensional (3D) images of biological samples. However, it requires acquisition of many images per volume, and is therefore time consuming, and may not be suitable for live cell 3D imaging. We propose the use of the modified Gerchberg-Saxton phase retrieval algorithm to enable full 3D...
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... 2(a,c) are the two images used for the GS algorithm. Figure 3(a) presents the correlation graph between the original captured images and the reconstructed one using the GS algorithm as a function of the iteration number. The correlation exceeds 90% after only 5 iterations, and is almost 100% at 500 iterations. ...
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... correlation exceeds 90% after only 5 iterations, and is almost 100% at 500 iterations. Figure 3(b) is the image that was generated by FSP of the reconstructed field for a distance of 4 μm, and Fig. 3(c) is the originally generated image at distance of 4 μm. The correlation coefficient between Fig. 3(b,c) is 99.998%. ...
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... the correlation graph between the original captured images and the reconstructed one using the GS algorithm as a function of the iteration number. The correlation exceeds 90% after only 5 iterations, and is almost 100% at 500 iterations. Figure 3(b) is the image that was generated by FSP of the reconstructed field for a distance of 4 μm, and Fig. 3(c) is the originally generated image at distance of 4 μm. The correlation coefficient between Fig. 3(b,c) is 99.998%. Figure 4(a) shows the correlation graphs for different NAs and magnifications. All of the correlation graphs converge to 1, but the number of iterations required to achieve this value changes as a function of the NA and ...
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... algorithm as a function of the iteration number. The correlation exceeds 90% after only 5 iterations, and is almost 100% at 500 iterations. Figure 3(b) is the image that was generated by FSP of the reconstructed field for a distance of 4 μm, and Fig. 3(c) is the originally generated image at distance of 4 μm. The correlation coefficient between Fig. 3(b,c) is 99.998%. Figure 4(a) shows the correlation graphs for different NAs and magnifications. All of the correlation graphs converge to 1, but the number of iterations required to achieve this value changes as a function of the NA and magnification. Higher magnification produces a more detailed image, resulting in more rapid convergence ...
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... = 2μm with respect to Fig. 9(b)) was reconstructed, and is shown in Fig. 9(d). The corresponding experimental image acquired at the same Δz = 2μm is shown in Fig. 9(e), and the simi- larity to the reconstructed image validates the proposed approach. In addition, the captured images have a visible resemblance to the simulated model presented in Fig. ...
Citations
... Iterative phase retrieval has evolved into a tool to recover a complexvalued image by algorithmic post processing, providing a detailed visualization of material and biological specimen [1][2][3][4][5] . Fundamental to progress in these algorithms has sparked a revolution in the application of astronomy [6] , super resolution [7] , electron microscope [8] , 3D imaging [9][10][11][12] , and optical encryption [13][14][15][16] . Traditional methods, such as GS algorithm [2] and hybrid input-output algorithm [3] both require a finite object support. ...
... Coherent diffractive imaging (CDI), as a representative lensless imaging technique, aims at reconstructing a sample in two or three dimensions by virtue of magnitude data in the diffraction filed alone without the help of reference beam [1][2][3][4][5][6] . Iterative phase retrieval algorithms with an eye to a priori knowledge of exit wave have been commonly applied in CDI for post-processing image synthesis [7][8][9][10][11] . Although generating an excellent result, these methods have been trapped in a dilemma whether or not the imposed support constraint is tight. ...
As a coherent diffractive imaging technique, axial multi-image phase retrieval utilizes a series of diffraction patterns on the basis of axial movement diversity to reconstruct full object wave field. Theoretically, fast convergence and high-accuracy of axial multi-image phase retrieval are demonstrated. In experiment, its retrieval suffers from the tilt illumination, in which diffraction patterns will shift in the lateral direction as the receiver traverses along the axis. In this case, the reconstructed result will be blurry or even mistaken. To solve this problem, we introduce cross-correlation calibration to derive the oblique angle and employ tilt diffraction into axial phase retrieval to recover a target, which is successfully demonstrated in simulation and experiment. Also, our method could provide a useful guidance for measuring how obliquely the incident light illuminates in an optical system.
Light sheet fluorescence microscopy (LSFM) is a powerful technique that can provide high resolution images of biological samples. Therefore, this technique offers significant improvement for 3D imaging of living cells. However, producing high‐resolution 3D images of a single cell or biological tissues, normally requires high acquisition rate of focal planes, which means a large amount of sample sections. Consequently, it consumes a vast amount of processing time and memory, especially when studying real‐time processes inside living cells. We describe an approach to minimize data acquisition by interpolation between planes using a phase retrieval algorithm. We demonstrate this approach on LSFM datasets and show reconstruction of intermediate sections of the sparse samples. Since this method diminishes the required amount of acquisition focal planes, it also reduces acquisition time of samples as well. Our suggested method has proven to reconstruct unacquired intermediate planes from diluted datasets up to 10x fold. The reconstructed planes were found correlated to the original pre‐acquired samples (control group) with correlation coefficient of up to 90%. Given the findings, this procedure appears to be a powerful method for inquiring and analyzing biological samples.
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