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An optimization algorithm for phase-only hologram (POH) based on the point source method (PSM) with the holographic viewing-window (HVW) by using a convolutional neural network (CNN) is proposed. The network training adopts an unsupervised strategy that maps the target image to optimized constant phases (OCPs) corresponding to the wavefront of each point source instead of random constant phases (RCPs), and then the optimized POH can be obtained by wavefront superposition. The simulation and experimental results show that the speckle noise of the reconstruction is significantly suppressed compared with the initial random phase method (RPM), which demonstrates the feasibility of the proposed method and shows a strong generalization ability of the trained CNN. The HVW of a holographic near-eye display (NED) has a continuous observation range of about 6 mm in the horizontal direction without using additional optical expansion elements, and a complete high-quality image can be observed within this range.

Convolutional Neural Network for Phase-Only Hologram Optimization Based on the Point Source Method With the Holographic Viewing-Window