Convolutional neural network transcendentally enhanced phase retrieval detection system for intermediate frequency errors of optical elements

Abstract

The invention discloses a convolutional neural network transcendentally enhanced phase retrieval detection system for intermediate frequency errors of optical elements. According to the system, a calibrated spatial light modulator is utilized to generate phase modulation of intermediate frequency errors; modulation light is projected on an imaging camera for reception, so as to obtain multiple data pairs of strength patterns and intermediate frequency error description items to serve as a training data set of a neural network; and a trained model is used for detecting real intermediate frequency errors. Compared with data obtained by adoption of simulation, the model trained by the data provided by the system is more suitable for recovering practical intermediate frequency errors. According to the system, initial de-optimization of a phase retrieval intermediate frequency error detection technology is realized; and by utilizing a convolutional neural network model in deep learning, a relationship between the strength patterns after intermediate frequency error modulation and error distribution is established to predict phase distribution of the intermediate frequency errors, and the result serves as an initial solution of a phase recovery algorithm, so that the convergence performance of the algorithm is effectively improved and the convergence speed is improved.

Description

technical field [0001] The invention relates to a priori enhanced intermediate frequency error phase recovery detection system in the field of computational imaging, in particular to a convolutional neural network priori enhanced optical element intermediate frequency error phase recovery detection system. Background technique [0002] Optical components with large aperture and small F number are widely used in high-power laser systems such as inertial confinement fusion, and these systems have higher requirements for the surface quality of optical components. However, in the process of precision machining and polishing of optical components, due to the tip radius of the processing tool, processing method, vibration and thermal drift, etc., a periodic structure with a certain frequency will be left on the surface of the component, which is called the surface of the optical component. Errors, the existence of these surface errors will have a significant impact on the transmis...

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Problems solved by technology

[0004] However, during the operation of its iterative algorithm, the phase recovery technology often cannot correctly converge to the global optimal solution, but stagnates at the local minimum, which makes the surface shape results obtained by the detection system less accurate

In addition, when the initial solution of the surface error does not have sufficient prior information, the calculation process of the phase recovery detection system often takes too long to run, and sometimes even fails to converge

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