A Shack-Hartmann Wavefront Detector Based on Deep Learning

Abstract

The invention discloses a Shack-Hartmann wavefront detector based on deep learning, by redesigning the wavefront reconstruction algorithm of the traditional Shack-Hartmann wavefront detector, using the convolutional neural network algorithm to replace the traditional algorithm, and using strong noise data to The integrated neural network training can not only accurately reconstruct the wavefront under strong noise, but also has obvious advantages in operation speed compared with traditional methods because the algorithm is end-to-end wavefront detection. After meeting these two design requirements, the present invention can solve the problem that in the two-photon microscopy system, as the imaging depth inside the biological tissue deepens, the strong noise generated by the tissue makes the wavefront unable to be accurately detected, and can meet the requirements of the two-photon microscopy system. The requirements of the microscopic system for the detection speed of the wavefront, and these two points are the factors that have always limited the application of the wavefront detector in the two-photon microscopic system. significance.

Description

technical field [0001] The invention belongs to the technical field of light wave front detection, in particular to a Shack-Hartmann wave front detector based on deep learning. Background technique [0002] Two-photon excitation microscopy (Two-photon Excitation Microscopy) is a fluorescence imaging technique that can image living tissue to a depth of about 1 mm. It is different from conventional fluorescence microscopy in that the excitation wavelength is shorter than the emission wavelength because the two excitation wavelengths The wavelength of the photons is longer than the wavelength of the resulting emitted light. Two-photon excitation microscopy typically uses near-infrared excitation light, which can also excite fluorescent dyes, however two photons of infrared light are absorbed for each excitation. The use of infrared minimizes scattering in tissue, and background signal is strongly suppressed due to multiphoton absorption, both effects leading to increased penet...

AI Technical Summary

Problems solved by technology

However, the centroid of the lattice is first calculated through the neural network, and the wavefront is reconstructed with the centroid, which also reduces the calculation speed of the entire algorithm.

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