A Microscope Image Deconvolution Acceleration Algorithm Based on Mismatch Operator

Abstract

The invention discloses a microscope image deconvolution acceleration algorithm based on a mismatch operator, which helps the fluorescence microscope image to be deconvoluted by introducing a mismatch back-projection operator into the traditional Richardson-Lucy algorithm The operation, by calculating the corresponding back-projection operator with the fastest convergence efficiency, and combining with the Butterworth filter, will cause artifacts to be removed from the frequencies exceeding the diffraction limit, maintaining the deconvolution effect of the algorithm, in each iteration While the calculation time is almost constant, the number of deconvolution iterations required is reduced. In most cases, only one iteration is needed to obtain the ideal deconvolution effect. Therefore, the present invention effectively designs a back-projection operator, which improves the problems that the number of iterations is difficult to determine, the calculation time is long, and the calculation cost is high in the iterative deconvolution process of the computer.

Description

technical field [0001] The invention belongs to the technical field of fluorescence microscopic imaging, and in particular relates to a microscope image deconvolution acceleration algorithm based on a mismatch operator. Background technique [0002] Fluorescence microscopy is a very useful imaging technology in the biological field. Using fluorescent protein markers or specific proteins inside biological samples, biological samples can emit fluorescence under light excitation. Recording this fluorescence can study specific properties of biological samples. Its application range Extend from single cell imaging to large tissue imaging. Fluorescence microscopy has submicron spatial resolution, molecular specificity, and high contrast. These properties allow us to directly explore the structure and function of biological samples, but inherent blurring and noise degrade fluorescence data, which can affect the researcher's understanding of real biological samples. As long as the ...

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Problems solved by technology

However, the introduction of the virtual viewing angle of this method determines that it is only applicable to multi-view fluorescence microscopy data in theory, and how to apply this technique to more general single-view fluorescence microscopy data is a problem to be solved; on the other hand, although Compared with the traditional Richardson-Lucy deconvolution, this method improves the efficiency by 3 times, but the problem of "stopping criterion" in the iterative process is still not effectively solved

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