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Hartmann wavefront sensor mode wavefront restoration method based on deep learning

A deep learning and sensor technology, applied in the field of optical information measurement, can solve problems such as limitations, and achieve the effect of reducing mode coupling error and mode confusion

Active Publication Date: 2019-07-23
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

However, the iterative process required by this method will limit its application in scenarios with high real-time requirements such as adaptive optics.

Method used

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  • Hartmann wavefront sensor mode wavefront restoration method based on deep learning
  • Hartmann wavefront sensor mode wavefront restoration method based on deep learning
  • Hartmann wavefront sensor mode wavefront restoration method based on deep learning

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Embodiment Construction

[0042] An embodiment of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0043] figure 1 It is a principle flowchart of a Hartmann wavefront sensor mode wavefront restoration method based on deep learning according to the present invention, which mainly includes computer simulation to generate a training data set process and an actual measurement process. figure 2 It is a sub-aperture arrangement of a 19-unit Hartmann wavefront sensor, the entrance pupil function of the microlens is hexagonal, and the outer circle is the entrance pupil function. Such as figure 2 As shown, this embodiment uses a Hartmann wavefront sensor comprising 19 sub-apertures. Assume that the light source wavelength λ used in this embodiment is 500nm; the entrance pupil of the Hartmann wavefront sensor is a circular function P(x 0 ,y 0 ); the spacing a of the microlens in the x direction is 0.37mm, and the spacing b in the y direction is about ...

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Abstract

The invention provides a Hartmann wavefront sensor mode wavefront restoration method based on deep learning. An image collected by a focal plane camera of the Hartmann wavefront sensor is used as input, a trained artificial neural network is adopted for operating the image, and a modal coefficient of the Hartmann wavefront sensor before entering of optical wave is obtained directly. Compared withthe traditional wavefront restoration method based on a subaperture average slope, the method of the invention can process more information including subaperture spot position and spot pattern in thefocal plane camera image, and can more effectively reduce errors of mode confusion and mode coupling caused by relying on average slope alone. The method can restore a higher-order modal coefficient with higher accuracy under the same measurement noise strength of the focal plane camera.

Description

technical field [0001] The invention belongs to the technical field of optical information measurement, and relates to a method for measuring optical wavefronts, in particular to a method for restoring Hartmann wavefront sensor mode wavefronts based on deep learning. Background technique [0002] The modal wavefront reconstruction methods of the existing Hartmann wavefront sensors usually reconstruct the coefficients of the aberration mode according to the average slope of each sub-aperture (Guang-ming Dai, “Modal wave-front reconstruction with Zernikepolynomials and Karhunen–Loève functions, "J. Opt. Soc. Am. A13, 1218-1225, 1996). The acquisition of the average slope depends on the calculation of the centroid of the sub-aperture image, and each sub-aperture usually only obtains two slopes in the x and y directions. This is true when the wavefront aberrations of the subaperture incident light only contain tilt, without defocus and higher order aberrations, however when the...

Claims

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Application Information

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IPC IPC(8): G01J9/00G06N3/02G06N3/08
CPCG01J9/00G01J2009/002G06N3/02G06N3/08
Inventor 郭友明田雨饶学军饶长辉
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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