Wave front correction method based on defocused image training

Abstract

The invention discloses a wave front correction method based on defocused image training, and a device used by the method comprises an image acquisition module, a neural network module, a voltage conversion module and an actuating mechanism. The method comprises the following steps that the incident light is converged into defocused light spots through an optical prism; the image acquisition module is responsible for acquiring defocused light spots; after a large number of defocused light spot image training, the neural network module fits corresponding Zernike coefficients according to the image characteristics transmitted by the image acquisition module; the voltage conversion module establishes the relationship between the Zernike coefficient and a driving voltage; and finally, an execution mechanism realizes the correction of the wave front distortion generated by the atmospheric turbulence according to the fitted Zernike coefficients. The wave front correction method based on defocused image training does not need to use a Hartmann wave front detector and the like, and improves the utilization rate of light beam energy. The loop iteration process is not needed, the bandwidth is high, and the requirement of real-time performance is met. Only a single defocused image is needed to be collected for neural network training, so that the workload of image collection is reduced, and the information quantity of sample light spots is increased.

Description

technical field [0001] The invention relates to the field of wavefront correction, in particular to a wavefront correction method based on defocused image training, which is mainly used for correcting wavefront distortion generated by atmospheric turbulence. Background technique [0002] Wavefront correction technology is a technique for correcting the wavefront aberration of light field, which has been widely used in the field of adaptive optics in space optical communication and astronomical imaging. The wavefront correction technology with wavefront detection uses the Hartmann detector to measure the wavefront of the received beam, and then drives the deformable mirror to correct the wavefront distortion. It has the advantages of high precision and high bandwidth, but the Hartmann detector does not The energy requirements are high, resulting in most of the received beam being split into the Hartmann detector, and the scope of application is limited. [0003] The wavefron...

AI Technical Summary

Problems solved by technology

Although the training neural network with a single focus map has the advantage of convenient sample collection, the sample information overlaps in the central part, which is not conducive to the identification and extraction of target features, resulting in low prediction accuracy

Although the training method of two images (one in-focus image and one out-of-focus image) can improve the prediction accuracy of the neural network, it also has the problems of complex detection optical path and cumbersome sample collection.

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