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

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.