Phase shift error estimation based on deep learning

Phase-shifting interferometry is a high-precision and commonly used phase retrieval method. In practical applications, phase shift errors are usually introduced due to factors such as environmental disturbances and phase shifter error. In this paper, we propose a deep learning method for estimating phase shift error from phase-shifting interferograms. This method manages to process the three interferograms with phase shift π/2, and uses neural network to extract phase shift errors from three interferograms. The analysis shows that the proposed method can effectively estimate the phase shift error under the noisy interferograms. This method can be used to correct phase-shift errors for phase retrieval (e.g., the least squares phase retrieval method) and calibrate phase shifters.