Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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3D Visualization for Extremely Dark Scenes Using Merging Reconstruction and Maximum Likelihood Estimation

  • Lee, Jaehoon (Department of Computer Science and Networks, Kyushu Institute of Technology) ;
  • Cho, Myungjin (School of ICT, Robotics, and Mechanical Engineering, IITC, Hankyong National University) ;
  • Lee, Min-Chul (Department of Computer Science and Networks, Kyushu Institute of Technology)
  • Received : 2021.01.25
  • Accepted : 2021.04.15
  • Published : 2021.06.30
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Abstract

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1F1A1068637).

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