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Single-frame focal plane light intensity image deep learning phase difference method based on grating modulation

A technology of image depth and phase difference method, applied in the field of phase inversion, can solve the problems of limited application and complex system structure, and achieve the effect of avoiding the iterative process and improving the calculation efficiency.

Pending Publication Date: 2021-01-05
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

However, this method requires non-redundant masks to enter and exit the optical path multiple times, and the system structure is complex. The application of this algorithm in actual wavefront detection is limited.

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  • Single-frame focal plane light intensity image deep learning phase difference method based on grating modulation
  • Single-frame focal plane light intensity image deep learning phase difference method based on grating modulation
  • Single-frame focal plane light intensity image deep learning phase difference method based on grating modulation

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Embodiment Construction

[0026] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific implementation cases and with reference to the accompanying drawings.

[0027] figure 1 It is a schematic diagram of the working principle of a single-frame focal plane light intensity image deep learning phase difference method based on grating modulation. figure 2 It is a work flow diagram of the present invention, and the concrete implementation process is:

[0028] Step 1: Design a wavefront sensor based on off-focus grating modulation, use the off-axis Fresnel zone plate in close contact with the short focal length lens, and place the CCD at the focal plane of the short focal length lens. The relevant parameters of the defocus grating are as follows: the side length of the defocus grating is 16mm, the focal length of the defocus grating is 7.5m, the displacement of the twisted...

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Abstract

The invention discloses a single-frame focal plane light intensity image deep learning phase difference method based on grating modulation. A data set fully sampled in a sample space is selected for aconvolutional neural network (CNN) to fit a mapping relationship between a far-field light spot and a near-field wavefront phase, after network training convergence, a far-field light spot image is input to obtain a wavefront aberration corresponding to the far-field light spot image, the mapping solving process does not need iterative operation any more, and the calculation time consumption is reduced. The focal plane wavefront restoration sensor which is simple in structure, good in real-time performance and high in restoration precision is designed on the basis of grating modulation, the iterative optimization process of a traditional phase difference method is avoided through a deep learning algorithm, algorithm time consumption is reduced, and rapid phase inversion of a single-framefocal plane light intensity image is achieved.

Description

technical field [0001] The present invention relates to the technical field of phase inversion methods, in particular to a deep learning phase difference method based on grating modulation for single-frame focal plane light intensity images. Background technique [0002] The phase inversion technology directly reconstructs the wavefront phase information according to the far-field light intensity distribution, which is not sensitive to the environment and does not require a wavefront sensor. The Gerchberg-Saxton (GS) algorithm is a class of classic phase inversion methods. The GS algorithm uses the angular spectrum transmission theory to iteratively calculate the wavefront aberration according to the Fourier transform relationship between the far-field complex amplitude and the near-field complex amplitude. Its structure is simple and easy to implement. To solve the problem, the GS algorithm is easy to fall into the local extremum, and the convergence accuracy is low. [0...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01J9/00G06N3/04G06N3/08
CPCG01J9/00G06N3/08G01J2009/002G06N3/045
Inventor 邱学晶赵旺杨超王帅许冰
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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