Viewing resolution and depth-of-field enhancement for a digital 3D display based on neural network-enabled multilayer view perspective fitting

Abstract

The traditional digital three-dimensional (3D) display suffers from low resolution and a narrow depth of field (DoF) due to the lack of planar pixels transformed into view perspectives and the limitation of the diffraction effect of the lens, respectively, which are the main drawbacks to restrict the commercial application of this display technology. Here, the neural network-enabled multilayer view perspective fitting between the reconstructed and original view perspectives across the desired viewing depth range is proposed to render the optimal elemental image array (EIA) for enhancing the viewing resolution as well as the DoF of the digital 3D display. Actually, it is an end-to-end result-oriented coding method to render the fusion EIA with optimal multidepth fusion and resolution enhancement with high registration accuracies for both view perspective and depth reconstructions by using a depth-distributed fitting neural network paradigm. The 3D images presented in the simulations and optical experiments with improved viewing resolution and extended viewing depth range are demonstrated, verifying the feasibility of the proposed method.