Information theoretic performance evaluation of 3D integral imaging

Integral imaging (InIm) has proved useful for three-dimensional (3D) object sensing, visualization, and classification of partially occluded objects. This paper presents an information-theoretic approach for simulating and evaluating the integral imaging capture and reconstruction process. We utilize mutual information (MI) as a metric for evaluating the fidelity of the reconstructed 3D scene. Also we consider passive depth estimation using mutual information. We apply this formulation for optimal pitch estimation of integral-imaging capture and reconstruction to maximize the longitudinal resolution. The effect of partial occlusion in integral imaging 3D reconstruction using mutual information is evaluated. Computer simulation tests and experiments are presented. (C) 2022 Optica Publishing Group under the terms of the Optics Open Access Publishing Agreement

Automated summary

This paper presents an information-theoretic approach for simulating and evaluating the integral imaging capture and reconstruction process. Mutual information is used as a metric for evaluating the fidelity of the reconstructed 3D scene. The paper also considers passive depth estimation using mutual information and evaluates the effect of partial occlusion in integral imaging 3D reconstruction.