Structured light illumination super-resolution microscopic imaging system and method based on machine learning

Abstract

The invention discloses a structured light illumination super-resolution microscopic imaging system and method based on machine learning. The structured light illumination super-resolution microscopicimaging system based on machine learning is sequentially distributed along the optical path direction to a digital micromirror device DMD, a mirror M, a first lens L1, an active phase modulation system APM, a second lens L2, and a charge coupled device CCD. The structured light illumination super-resolution microscopic imaging method based on machine learning comprises the following steps: usinga distorted stripe pattern and the corresponding scattering phase reconstruction coefficient to form a training data set; and then constructing a corresponding learning model through a deep convolutional neural network algorithm, so that the distorted stripe pattern with an unknown scattering phase reconstruction coefficient is quickly corrected, and a high-precision corrected stripe pattern is obtained. The invention performs aberration correction on the stripe structured light in the structured light illumination microscope; the speed is fast, and the precision is high; and the invention canimprove the imaging depth of the microscope system, optimize the imaging quality, and expand the application range of the structured light illumination microscope.

Description

technical field [0001] The invention belongs to an adaptive optical wavefront aberration correction method in the field of super-resolution microscopy imaging, and in particular relates to a machine learning-based system for high-speed adaptive optical correction of aberrations for fringe patterns in structured illumination microscopy And methods to improve the imaging depth of the microscope system and optimize the imaging quality, and expand the application range of structured light illumination microscope. Background technique [0002] In life science research, fluorescence microscopy is widely used due to its advantages of specific labelability and real-time imaging of living cells. However, due to the limitation of the "diffraction limit" of fluorescence microscopy, its use in the field of life medical research is greatly limited. In recent years, researchers have proposed super-resolution microscopy techniques that can break through the "diffraction limit", including ...

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Problems solved by technology

[0006] In order to solve the scattering and aberration problems of structured light illumination microscopy in deep tissue imaging, the present invention provides a structured light illumination super-resolution microscopic imaging system and method based on machine learning

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