Deep learning-based single-shot lateral shearing interferometry

• A novel lateral shearing interferometry (LSI) method employs deep-learning to enable single-shot surface measurement. • Two x- and y- shearing modules are attached together to generate a single composite fringe pattern. • Data for deep learning training are automatically obtained by using a deformable mirror and polarization camera. • Deep learning-based LSI can experimentally measure both simple and complex surfaces with a single-shot measurement after training. Lateral shearing interferometry (LSI) is a powerful measurement method for wavefront sensing and optical testing. However, traditional LSI methods often face challenges in terms of complicated system configurations and vibration sensitivity. In this paper, we propose a novel approach that leverages deep learning to enable single-shot LSI for surface measurement. In our LSI system, the x - and y -directional shearing modules are attached together and a polarization grating and a polarization camera are utilized to obtain a single composite interferogram, which is the summation of the x - and y -directional shearing interferograms. Deep learning is then employed to accurately obtain the x - and y -phases (which are directly related to the surface slope) from the single composite interferogram, significantly reducing the effect of vibration and improving the robustness of the measurements. We trained a deep learning network using training data obtained from a deformable mirror so that the trained network knows how to retrieve the x - and y -phases from a single composite interferogram. We demonstrate the effectiveness of our approach through experimental measurement of different surfaces ranging from simple concave to complex random surfaces, and show that our deep learning-based LSI enables single-shot and even dynamic surface measurement. This work opens new avenues for the application of artificial intelligence in LSI to enable high-speed and dynamic measurement of specular surfaces.