Phase retrieval algorithm based on the neural network and the GS

Hong Cheng II; Yong Liu; Rui Wang V; Quanbing Zhang II

doi:10.1117/12.2536648

18 November 2019 Phase retrieval algorithm based on the neural network and the GS

Hong Cheng II, Yong Liu, Rui Wang V, Quanbing Zhang II

Proceedings Volume 11188, Holography, Diffractive Optics, and Applications IX; 1118809 (2019) https://doi.org/10.1117/12.2536648
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

Abstract

Optical waves can be described by intensity and phase. However, optical waves oscillate too fast for detectors to measure anything but time-averaged intensities. This is unfortunate since the phase can reveal important information about the object. Therefore, it is necessary to apply the known intensity information to retrieve the phase information, which is called phase retrieval. As a classical phase retrieval algorithm, the Gerchberg-Saxton iteration method has the characteristics of continuous error reduction, but a large number of iterations are needed to obtain high-quality retrieval results. The field of neural network algorithm was initially inspired by the goal of modeling biological neural systems, but then parted ways and became an engineering problem with good results in the field of machine learning. This kind of network relies on the complexity of the system to process information by adjusting the interconnection among a large number of internal nodes. A new algorithm combined the neural network and the Gerchberg-Saxton iterative is proposed. Firstly, the initial phase is obtained by Gerchberg-Saxton iteration method, and then a good training model is obtained by using paired initial phase and precise phase training neural network. For the samples in the test set, the trained model is applied to the phase retrieval results of Gerchberg-Saxton iteration method to obtain more accurate phase results. Experiments proved that the better retrieval results with a few iterations can be acquired.

Citation Download Citation

Hong Cheng II, Yong Liu, Rui Wang V, and Quanbing Zhang II "Phase retrieval algorithm based on the neural network and the GS", Proc. SPIE 11188, Holography, Diffractive Optics, and Applications IX, 1118809 (18 November 2019); https://doi.org/10.1117/12.2536648

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KEYWORDS

Phase retrieval

Evolutionary algorithms

Neural networks

Detection and tracking algorithms

Adaptive optics

Systems modeling

Diffraction

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