An element image array generation algorithm for enhancing the depth of field quality of 3D reproduction based on multi-depth fusion

Abstract

Monocular-vision-based integral imaging (InIm) offers significant potential for three-dimensional (3D) visualization, enabling naked-eye 3D viewing through a straightforward acquisition process followed by computational imaging. However, the stacking of diffused circles during 3D reconstruction results in a narrow depth-of-field (DOF) range for high-quality display, limiting the widespread adoption of this technology. To address this limitation and enhance display quality, this study presents a multi-depth fusion-based algorithm for generating element image arrays (EIAs). The proposed algorithm leverages the depth information of the 3D scene and display device parameters to construct an adaptive hierarchical model. By incorporating characteristics of the human visual system (HVS) and light field depth cues, it introduces a depth-difference-driven Gaussian fusion coding method. The resulting EIA achieves enhanced 3D reproduction quality within a specified depth range. Simulation and reconstruction experiments were performed on the system's center depth plane (CDP) and two extreme DOF planes. Results demonstrate that the proposed algorithm outperforms comparative methods in the objective metrics of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), validating its effectiveness.