Kim Hyun-Woo, Lee Min-Chul, Cho Myungjin
Department of Computer Science and Networks, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi 820-8502, Fukuoka, Japan.
School of ICT, Robotics, and Mechanical Engineering, Hankyong National University, IITC, 327 Chungang-ro, Anseong 17579, Kyonggi-do, Republic of Korea.
Sensors (Basel). 2024 Mar 7;24(6):1731. doi: 10.3390/s24061731.
In this paper, we propose a method for the three-dimensional (3D) image visualization of objects under photon-starved conditions using multiple observations and statistical estimation. To visualize 3D objects under these conditions, photon counting integral imaging was used, which can extract photons from 3D objects using the Poisson random process. However, this process may not reconstruct 3D images under severely photon-starved conditions due to a lack of photons. Therefore, to solve this problem, in this paper, we propose -observation photon-counting integral imaging with statistical estimation. Since photons are extracted randomly using the Poisson distribution, increasing the samples of photons can improve the accuracy of photon extraction. In addition, by using a statistical estimation method, such as maximum likelihood estimation, 3D images can be reconstructed. To prove our proposed method, we implemented the optical experiment and calculated its performance metrics, which included the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), peak-to-correlation energy (PCE), and the peak sidelobe ratio (PSR).
在本文中，我们提出了一种在光子匮乏条件下使用多次观测和统计估计对物体进行三维（3D）图像可视化的方法。为了在这些条件下对3D物体进行可视化，采用了光子计数积分成像，它可以利用泊松随机过程从3D物体中提取光子。然而，由于光子不足，该过程在严重光子匮乏条件下可能无法重建3D图像。因此，为了解决这个问题，在本文中，我们提出了带有统计估计的多次观测光子计数积分成像。由于光子是使用泊松分布随机提取的，增加光子样本可以提高光子提取的准确性。此外，通过使用统计估计方法，如最大似然估计，可以重建3D图像。为了证明我们提出的方法，我们进行了光学实验并计算了其性能指标，包括峰值信噪比（PSNR）、结构相似性（SSIM）、峰值相关能量（PCE）和峰值旁瓣比（PSR）。
