Off-axis telescope low-order aberration correction method based on deep learning

An aberration correction and deep learning technology, applied in neural learning methods, optics, optical components, etc., can solve the problems of low installation and adjustment efficiency, inability to apply, and large amount of calculation, so as to reduce the training time and improve the installation and adjustment efficiency. And the effect of precision and strong universality

Active Publication Date: 2022-08-05
INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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Problems solved by technology

This method has a large amount of calculation, low adjustment efficiency, and lack of real-time performance. At the same time, when the secondary mirror has a large amount of misalignment, the misalignment amount and the Zernike polynomial coefficient representing the system aberration are no longer linear, and the solution error is large. , cannot be applied to actual adjustment

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  • Off-axis telescope low-order aberration correction method based on deep learning
  • Off-axis telescope low-order aberration correction method based on deep learning
  • Off-axis telescope low-order aberration correction method based on deep learning

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Embodiment Construction

[0037] The specific implementation steps of the present invention will be further described below in conjunction with the accompanying drawings.

[0038] like figure 1 As shown in the flow chart, the low-order aberration correction method for telescopes based on deep learning is mainly divided into two parts. figure 1 The left part is the preparatory work of the telescope aberration correction system, that is, the training process of the weight parameters of the neural network model, and the right part is the aberration correction process of the actual system, which includes the following steps:

[0039] Step 1 Establish the optical system model:

[0040] In the optical simulation software Zemax, the optical system model of the off-axis reflecting telescope system to be installed is established according to the structural parameters;

[0041] Step 2 Acquisition of neural network data set (including training set and test set):

[0042] After the off-axis reflective optical s...

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Abstract

The invention discloses an off-axis telescope low-order aberration correction method based on deep learning. The method comprises the following steps: step 1, establishing an optical system model; 2, constructing a neural network data set, wherein the data set comprises a training set and a test set; 3, training a neural network model; and step 4, solving the misalignment amount and guiding installation and adjustment. According to the method, the aberration of the optical system can be obtained without using a wavefront sensor, the complexity of the system is effectively reduced, and accumulative errors caused by an aberration detection link are avoided, so that the low-order aberration correction efficiency of the system and the imaging quality of the system are improved. The method is suitable for solving the lens misalignment amount of various complex systems, has high precision and real-time performance, and has important guiding significance when being applied to aberration correction of an off-axis telescope in engineering practice.

Description

technical field [0001] The invention belongs to the field of aberration correction of off-axis telescopes, and particularly relates to a low-order aberration correction method for off-axis telescopes based on deep learning, aiming at the aberrations caused by the spatial position misalignment of optical lenses. Background technique [0002] Compared with the coaxial reflective optical system, the off-axis reflective optical system has no central blocking, and has the advantages of high light energy utilization and no mid-low frequency diffraction limit, so it has been widely used in the fields of space optical communication, astronomical detection and space remote sensing. Applications. However, due to residual errors after the initial assembly of optical components, changes in the external environment during system operation (such as temperature changes, airflow disturbances, and gravity effects, etc.), as well as self-jitter, the spatial relative position of the optical le...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G02B27/00G06V10/82G06V10/766G06V10/774G06N3/04G06N3/08
CPCG02B27/0012G02B27/0025G06V10/82G06V10/766G06V10/774G06N3/08G06N3/045
Inventor 黄永梅唐薇田思恒郭弘扬吴琼雁王子豪王强贺东
Owner INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI
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