Depth Discrimination Enhanced Computational Integral Imaging Using Random Pattern Illumination
Year: 2009
Type: article
Abstract: A method enhancing the depth discrimination of the computational integral imaging is proposed. The proposed method captures two sets of the elemental images under random pattern and regular uniform illumination. The captured two sets of real-valued elemental images are coded into one set of the complex-valued elemental images. Finally, the depth section image of the objects is reconstructed by applying computational integral imaging technique to the generated complex-valued elemental images. Experiment and simulation results show that the proposed method enhances the depth discrimination of the depth sectioning by suppressing blurred images in off-focused planes.
Authors: Ganbat Baasantseren, Jae-Hyeung Park, Nam Kim
Institution: Chungbuk National University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2034730756