A wavefront phase difference detection method based on long short-term memory deep network

Abstract

The invention discloses a wavefront phase difference detection method based on a long-short-term memory deep network, which includes: inputting characteristic parameters of an actual optical system and generating a training data set; generating a focus plane from the training data set according to the principle of Fourier optics PSF imagei 1 (x,y) and defocused PSF image i 2 (x, y); extract the PSF image i of the above-mentioned focal plane 1 (x,y) and defocused PSF image i 2 The eigenvectors of (x, y) are used as input data; the eigenvectors of the PSF image sequence collected from the actual optical system are extracted; the PSF image sequence is input into the trained convolutional neural network model according to the time sequence t to determine the phase of the wavefront distortion to obtain A series of distortion phase parameters for training; generate input data and output data for LSTM deep network training, initialize network parameters and repeatedly train LSTM deep network until its loss function converges.

Description

technical field [0001] The invention relates to the field of network model training, in particular to a wavefront phase difference detection method based on a long short-term memory deep network. Background technique [0002] The emergence of adaptive optics technology enables large-aperture telescopes to greatly overcome the random disturbance of the wavefront caused by the atmosphere, and wavefront phase difference detection technology is one of its key technologies. Phase diversity (PD) uses two cameras on the focal plane and defocus plane of the optical system to collect images of wavefront distortion, and uses digital image processing methods to calculate the wavefront in real time under the action of randomly expanding targets phase difference to reconstruct a high-definition target image. [0003] From the perspective of reference objects, there are three types of detectors with wavefront phase detection capabilities: (1) active lighting facilities inside the measuri...

AI Technical Summary

Problems solved by technology

However, the CNN-based PD technology also has a contradiction between accuracy and calculation amount, and it only recovers the image deviation caused by the wavefront phase distortion under special conditions.

The influence of the time-varying nature of seeing and atmospheric environment parameters on the resulting dynamic wavefront estimation is not considered, resulting in inconsistent results with the instantaneous disturbance of the atmosphere within the observation range of the telescope

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