基于自适应复合插值的光线投射算法

doi:10.16182/j.issn1004731x.joss.201811017

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (11): 4187-4194.doi: 10.16182/j.issn1004731x.joss.201811017

基于自适应复合插值的光线投射算法

曾艳阳, 裴庆庆, 李保锟

河南理工大学计算机学院,河南焦作 454000

收稿日期:2018-05-30 修回日期:2018-06-22 发布日期:2019-01-04

Ray-casting Algorithm Based on Adaptive Compound Interpolation

Zeng Yanyang, Pei Qingqing, Li Baokun

School of Computer, Henan Polytechnic University, Jiaozuo 454000, China

Received:2018-05-30 Revised:2018-06-22 Published:2019-01-04
About author:ZengYanyan(1987-), male, Gushi Henan, doctor, lecturer, research direction for the simulation method and application.
Supported by:
National Natural Science Foundation of China(61503124), Henan Science and Technology Agency Scientific and Technological Research (182102310935)

摘要/Abstract

摘要： 光线投射算法因其算法成熟、成像质量高而被广泛应用于体绘制技术中。为解决高质量图像带来的计算量大、绘制时间长等问题,该文在传统光线投射算法的基础上,针对体绘制过程中重采样点插值、合成耗时长问题进行研究,提出一种改进的自适应复合插值光线投射算法。依据设置的阈值范围,调整后退采样步长大小进行插值采样点以及插值算法的选取,优化重采样及插值过程。实验结果表明,在保证所需图像质量下,此文改进方法与传统体绘制算法相比速度获得显著提升。

关键词: 体绘制, 光线投射算法, 重采样, 复合插值

Abstract: The ray casting algorithm is widely used in volume rendering technology due to its mature algorithm and high imaging quality. To solve the problems of large amount of calculation and long rendering time caused by high-quality images, the problems of re-sampling point interpolation and time-consuming synthesis in the volume rendering process based on the traditional ray casting algorithm are studied, and an improved adaptive compound interpolation ray casting algorithm is proposed. According to the set threshold range, the size of the retreating sampling step is adjusted to select the interpolation sampling point and interpolation algorithm, and the re-sampling and interpolation process are optimized. The experimental results show that the speed of the improved method is significantly improved compared with the traditional volume rendering algorithm under the guarantee of the required image quality.

Key words: volume rendering, ray casting algorithm, re-sampling, compound interpolation

中图分类号:

TP391.9

曾艳阳, 裴庆庆, 李保锟. 基于自适应复合插值的光线投射算法[J]. 系统仿真学报, 2018, 30(11): 4187-4194.

Zeng Yanyang, Pei Qingqing, Li Baokun. Ray-casting Algorithm Based on Adaptive Compound Interpolation[J]. Journal of System Simulation, 2018, 30(11): 4187-4194.

参考文献

[1] Maciej Borzęcki, Adam Skurski, Marek Kamiński, et al.Applications of Ray-Casting in Medical Imaging[J]. Advances in Intelligent Systems\s&\scomputing (S2194-5357), 2014, 283(11): 3-14.
[2] Tang Zesheng.Visualization of three-dimensional data field [M]. Beijing: Tsinghua University Press, 1999.
[3] Yao J, Xue J, Lv K, et al.A parallel volume rendering method for massive data[C]// IEEE International Conference on Multimedia & Expo Workshops. IEEE, 2016: 1-6.
[4] Hassan A H, Fluke C J, Barnes D G.A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes[J]. Publications of the Astronomical Society of Australia (S1323-3580), 2012, 29(3): 340-351.
[5] Beyer J, Hadwiger M, Pfister H.State-of-the-Art in GPU-Based Large-Scale Volume Visualization[M]. UK: The Eurographs Association & John Wiley & Sons, Ltd. 2015.
[6] Cao Y, Ai Z, Wang H.A Distributed Multi-Node GPU Accelerated Parallel Rendering Scheme for Visualization Cluster Environment[C]// International Conference on Virtual Reality and Visualization. IEEE Computer Society, 2013: 153-160.
[7] Zhong J, Zou Z, Zhong J, et al.An Improved Fast Algorithm for Calculating the Gradient of Data Field[C]// International Conference, 2017: 1-6.
[8] M Levoy.Display of surfaces from volume data. IEEE Comput. Graph. Application (S0272-1716), 1988, 8(3): 29-37.
[9] Xing F, Kou B, Zhang L, et al.Design of a Control System for a Maglev Planar Motor Based on Two- Dimension Linear Interpolation[J]. Energies (S1996-1073), 2017, 10: 1132.
[10] Chae J K, Lee J Y, Lee M H, et al.Cubic Convolution Scaler Optimized for Local Property of Image Data[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society(S4796-4089), 2015, 24(12): 4796-4809.
[11] Mohanty F, Rup S, Dash B.A Thresholding-Based Salt and Pepper Noise Removal Using B-Spline Interpolation in MRI Images[C]// International Conference on Computational Intelligence and Communication Networks. IEEE, 2016: 246-251.

基于自适应复合插值的光线投射算法

Ray-casting Algorithm Based on Adaptive Compound Interpolation

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