Research Center for Hyper-Connected Convergence Technology, School of ICT, Robotics, and Mechanical Engineering, IITC, Hankyong National University, 327 Chungang-ro, Anseong 17579, Kyonggi-do, Republic of Korea.
Sensors (Basel). 2022 Nov 26;22(23):9199. doi: 10.3390/s22239199.
In this paper, we propose an enhancement of three-dimensional (3D) image visualization techniques by using different pickup plane reconstructions. In conventional 3D visualization techniques, synthetic aperture integral imaging (SAII) and volumetric computational reconstruction (VCR) can be utilized. However, due to the lack of image information and shifting pixels, it may be difficult to obtain better lateral and longitudinal resolutions of 3D images. Thus, we propose a new elemental image acquisition and computational reconstruction to improve both the lateral and longitudinal resolutions of 3D objects. To prove the feasibility of our proposed method, we present the performance metrics, such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and peak-to-sidelobe ratio (PSR). Therefore, our method can improve both the lateral and longitudinal resolutions of 3D objects more than the conventional technique.
在本文中，我们提出了一种通过使用不同的采集面重建来增强三维（3D）图像可视化技术的方法。在传统的 3D 可视化技术中，可以使用合成孔径积分成像（SAII）和体计算重建（VCR）。然而，由于图像信息的缺乏和像素的移动，可能难以获得更好的 3D 图像的横向和纵向分辨率。因此，我们提出了一种新的基本图像采集和计算重建方法，以提高 3D 物体的横向和纵向分辨率。为了证明我们提出的方法的可行性，我们给出了性能指标，如均方误差（MSE）、峰值信噪比（PSNR）、结构相似性（SSIM）和峰值旁瓣比（PSR）。因此，我们的方法可以比传统技术更有效地提高 3D 物体的横向和纵向分辨率。
