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在光子匮乏条件下使用比例光子估计的三维可视化

Three-Dimensional Visualization Using Proportional Photon Estimation Under Photon-Starved Conditions.

作者信息

Ha Jin-Ung, Kim Hyun-Woo, Cho Myungjin, Lee Min-Chul

机构信息

Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi 820-8502, Japan.

School of ICT, Robotics, and Mechanical Engineering, Hankyong National University, IITC, 327 Chungang-ro, Anseong 17579, Republic of Korea.

出版信息

Sensors (Basel). 2025 Feb 1;25(3):893. doi: 10.3390/s25030893.

DOI:10.3390/s25030893
PMID:39943532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11819839/
Abstract

In this paper, we propose a new method for three-dimensional (3D) visualization that proportionally estimates the number of photons in the background and the object under photon-starved conditions. Photon-counting integral imaging is one of the techniques for 3D image visualization under photon-starved conditions. However, conventional photon-counting integral imaging has the problem that a random noise is generated in the background of the image by estimating the same number of photons in entire areas of images. On the other hand, our proposed method reduces the random noise by estimating the proportional number of photons in the background and the object. In addition, the spatial overlaps have been applied to the space where photons overlap to obtain the enhanced 3D images. To demonstrate the feasibility of our proposed method, we conducted optical experiments and calculated the performance metrics such as normalized cross-correlation, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). For SSIM of 3D visualization results by our proposed method and conventional method, our proposed method achieves about 3.42 times higher SSIM than conventional method. Therefore, our proposed method can obtain better 3D visualization of objects than conventional photon-counting integral imaging methods under photon-starved conditions.

摘要

在本文中,我们提出了一种新的三维(3D)可视化方法,该方法在光子匮乏条件下按比例估计背景和物体中的光子数量。光子计数积分成像技术是光子匮乏条件下三维图像可视化技术之一。然而,传统的光子计数积分成像存在这样的问题,即通过在图像的整个区域估计相同数量的光子,会在图像背景中产生随机噪声。另一方面,我们提出的方法通过估计背景和物体中光子的比例数量来减少随机噪声。此外,空间重叠已应用于光子重叠的空间以获得增强的三维图像。为了证明我们提出的方法的可行性,我们进行了光学实验,并计算了诸如归一化互相关、峰值信噪比(PSNR)和结构相似性指数测量(SSIM)等性能指标。对于我们提出的方法和传统方法的三维可视化结果的SSIM,我们提出的方法实现的SSIM比传统方法高约3.42倍。因此,在光子匮乏条件下,我们提出的方法比传统的光子计数积分成像方法能够获得更好的物体三维可视化效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/2e92facc4799/sensors-25-00893-g018.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/53b4f448d56d/sensors-25-00893-g005.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/5d295eb72e94/sensors-25-00893-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/cabd8dd14e5f/sensors-25-00893-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/e25c0183d450/sensors-25-00893-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/0050cf7c94fa/sensors-25-00893-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/22efe2ef1d40/sensors-25-00893-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/987ee6fad6ba/sensors-25-00893-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/5a120131c68d/sensors-25-00893-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/62652763c471/sensors-25-00893-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/2e92facc4799/sensors-25-00893-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/7b9fe9bcf24c/sensors-25-00893-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/40f3f2893833/sensors-25-00893-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/53b4f448d56d/sensors-25-00893-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/a2a9bf15450d/sensors-25-00893-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/5d295eb72e94/sensors-25-00893-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/cabd8dd14e5f/sensors-25-00893-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/e25c0183d450/sensors-25-00893-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/0050cf7c94fa/sensors-25-00893-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/22efe2ef1d40/sensors-25-00893-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/987ee6fad6ba/sensors-25-00893-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/33b454ee9093/sensors-25-00893-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/72361a09380e/sensors-25-00893-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/e3577df9408c/sensors-25-00893-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/5a120131c68d/sensors-25-00893-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/62652763c471/sensors-25-00893-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d00a/11819839/2e92facc4799/sensors-25-00893-g018.jpg

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