Phase retrieval by extracting Zernike coefficients from two random phase-shifting interferograms based on deep learning

Abstract

This paper presents a method of extracting Zernike coefficients and then retrieving the phase based on deep learning. In the normal interferometry, the phase extraction algorithm is utilized to calculate the wrapped phase according to the registering phase shifting interferograms, and then unwrap it to obtain the phase map. Further, Zernike polynomials are used to fit the phase. The method proposed in this paper uses neural networks to extract 35 terms of Zernike coefficients from two random phase-shifting interferograms, then retrieve the phase based on Zernike polynomials. The method only needs two random phase-shifting interferograms, does not require phase extraction and unwrapping processing and greatly simplifies the computation for phase reconstruction. The paper presents the training processing, and provides the experimental results. The results show that the proposed method can reach high precision and are more suitable for the quick testing for workshop environment.