High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Interferometric wavefront reconstruction is important for applications such as precision optical metrology. Traditional methods are hindered by challenges such as inaccurate phase extraction from a single-shot measurement and error accumulation in multi-frame approaches, while reported deep learning methods often lack physical constraints and introduce additional errors during the phase unwrapping process. To address these issues, a physics-driven hybrid feature calibration network (HFC-Net) is proposed and optimized for extracting a high-precision wavefront from a single-shot interferogram. Simulation and experimental results demonstrate that the HFC-Net achieves root mean square (RMS) errors as low as 0.00031λ on simulated data and 0.0021λ on real data, with a processing time of 87.14 ms. Its efficiencies are validated by comparing it with the traditional Fourier transform method and reported deep learning models. The presented method is also valuable for phase object or dynamic aberration measurement.