Machine-learning-based high-speed lensless large-field holographic projection using double-sampling Fresnel diffraction method

Abstract

Machine learning can effectively accelerate the runtime of a computer-generated hologram. However, the angular spectrum method and single fast Fresnel transform-based machine learning acceleration algorithms are still limited in the field-of-view angle of projection. In this paper, we propose an efficient method for the fast generation of large field-of-view holograms combining stochastic gradient descent (SGD), neural networks, and double-sampling Fresnel diffraction (DSFD). Compared with the traditional Gerchberg–Saxton (GS) algorithm, the DSFD-SGD algorithm has better reconstruction quality. Our neural network can be automatically trained in an unsupervised manner with a training set of target images without labels, and its combination with the DSFD can improve the optimization speed significantly. The proposed DSFD-Net method can generate 2000-resolution holograms in 0.05 s. The feasibility of the proposed method is demonstrated with simulations and experiments.