Abstract
Limited by the resolution of display panel and residual aberrations of optical components, it is difficult for the three-dimensional light-field displays (3D LFDs) to provide viewers with awesome light field reproduction. Starting from the optical process of reproducing a 3D scene by LFD, a light field display degradation model is constructed to measure the display effect observed by viewer at a specific perspective. Considering the prevalent usage of deep learning techniques in the realm of light field image processing, a novel method based on parallax-view information synthesis and aberration precorrection is proposed to elevate the display performance. This method leverages the complementary parallax information between different perspectives parallax images to enhance the image quality of single-view and introduces a convolutional neural network to estimate the optical components aberrations. A series of optimized parallax images in multiple perspectives are generated and loaded on the display panel through coding, realizing the reproduction of original 3D light field. Simulation experiments and experiments on an LFD prototype verify the effectiveness of the proposed method which can achieve higher quality 3D reproduction.
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