Phase retrieval for single-frame interferogram with an irregular-shaped aperture based on deep learning

Abstract

This paper proposes a high-precision phase retrieval method based on deep learning to extract the Zernike coefficients from a single-frame interferogram with an irregular-shaped aperture. Once the Zernike coefficients are obtained, the phase distribution can be retrieved directly using the Zernike polynomials. For many apertures, the root mean square (RMS) of the residual wavefront between the true and estimated wavefronts reached the order of 10−3 λ. Simulations were conducted under different noise conditions, indicating that the proposed method has high measurement accuracy and robustness. Experiments demonstrated that the accuracy achieved by this method was comparable to that of commercial phase-shifting interferometers. We believe that this method is useful for measuring optical surfaces with irregular apertures.