激光全息投影三维图像重构技术
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摘要
为了提高激光全息投影三维图像重构后的统计信息表达能力,克服图像中强噪声对激光全息投影三维图形重构精度的影响,提出一种新的激光全息投影的三维图像重构方法。首先采用Ray-Casting图像空间扫描法进行激光全息成像投影,实现对待重构物体的全域空间扫描,然后对采集的激光全息投影图像进行光线投射,采用包围盒法求出光线进入数据体的三维坐标,构建图像重构的三维数据场,根据数据场对激光全息图像序列进行直接体绘制,实现三维图像重建。最后进行三维图像重构仿真测试,实验结果表明,采用该方法进行三维图像重构的统计信息输出的信噪比较高,图像的过渡平滑性较好,激光全息投影的光线遍历覆盖区域更全面,总体性能更优。
In order to improve the laser holographic projection 3 d image reconstruction after statistics expression ability,strong to overcome the image noise of laser holographic projection 3 d graphics reconstructed,the influence of accuracy in this paper,a new method of laser holographic projection of a 3 d image reconstruction.First the Ray-Casting for laser holographic imaging projection image space scanning method,implementation to reconstruct whole space scanning field of the object,then the light with the laser holographic projection image of the projection,the bounding box method is adopted to find the light into the 3 d coordinate data volume,build image reconstruction of 3 d data field,according to the data field to direct volume rendering of the laser holographic image sequence,realize 3 d image reconstruction.Finally reconstruct 3 d image simulation tests,the experimental results show that the method is adopted to improve the 3 d image reconstruction of high signal noise ratio(SNR)of the output of statistical information,images,the smoothness of the transition,the laser holographic projection light traverses the coverage is more comprehensive,better overall performance.
In order to improve the laser holographic projection 3 d image reconstruction after statistics expression ability,strong to overcome the image noise of laser holographic projection 3 d graphics reconstructed,the influence of accuracy in this paper,a new method of laser holographic projection of a 3 d image reconstruction.First the Ray-Casting for laser holographic imaging projection image space scanning method,implementation to reconstruct whole space scanning field of the object,then the light with the laser holographic projection image of the projection,the bounding box method is adopted to find the light into the 3 d coordinate data volume,build image reconstruction of 3 d data field,according to the data field to direct volume rendering of the laser holographic image sequence,realize 3 d image reconstruction.Finally reconstruct 3 d image simulation tests,the experimental results show that the method is adopted to improve the 3 d image reconstruction of high signal noise ratio(SNR)of the output of statistical information,images,the smoothness of the transition,the laser holographic projection light traverses the coverage is more comprehensive,better overall performance.
引文
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[3] 陈倩,潘中良.一种基于GPU加速的三维分形实时渲染方法[J].激光杂志,2016,37(11):136-140.
[4] SHEN L,SUN G,HUANG Q,et al.Multi-level discriminative dictionary learning with application to large scale image classification[J].IEEE Transactions on Image Processing,2015,24(10):3109-3123.
[5] 朱路,刘江锋,刘媛媛,等.基于稀疏采样与级联字典的微波辐射图像重构方法[J].微波学报,2014,30(6):41-45.
[6] PATEL V M,NGUYEN H V,and VIDAL R.Latent space sparse and low-rank subspace clustering[J].IEEE Journal of Selected Topics in Signal Processing,2015,9(4):691-701.
[7] 张惜,刘晓东.基于图像处理技术的激光直写导光板网点设计[J].激光技术,2015,39(6):751-755.
[8] HUANG Y,PAISLEY J,LIN Q,et al.Bayesian nonparametric dictionary learning for compressed sensing MRI[J].IEEE Transactions on Image Processing,2014,23(12):5007-5019.
[9] 于海琦,刘真,田全慧.一种基于RBF神经网络的打印机光谱预测模型[J].影像科学与光化学,2015,33(3):238-243.
[10] 柏猛,李敏花,吕英俊.基于对称性分析的棋盘图像角点检测方法[J].信息与控制,2015,44(3):276-283,290.
[11] 张子龙,薛静,乔鸿海,等.基于改进 SURF 算法的交通视频车辆检索方法研究[J].西北工业大学学报,2014,32(2):297-301.
[12] EVANGELIO R H,PATZOLD M,and KELLER I.Adaptively splitted GMM with feedback improvement for the task of background subtraction[J].IEEE Transactions on Information Forensics and Security,2014,9(5):863-874.
[13] MARTINS P,CASEIRO R,and BATISTA J.Non-parametric Bayesian constrained local models[C].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Columbus,OH,2014:1797-1804.
[14] BARNICH O and VAN D M.ViBe:a universal background subtraction algorithm for video sequences[J].IEEE Transactions on Image Processing,2011,20(6):1709-1724.
[15] ST-CHARLES P,BILODEAU G,and BERGEVIN R.SuBSENSE:a universal change detection method with local adaptive sensitivity[J].IEEE Transactions on Image Processing,2015,24(1):359-373.
[16] MOGHADAM A A,KUMAR M,and RADHA H.Common and Innovative visuals:a sparsity modeling framework for video[J].IEEE Transactions on Image Processing,2014,23(9):4055-4069.
[17] ALEXE B,DESELAERS T,and FERRARI V.Measuring the objectness of image windows[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(11):2189-2202.
[18] ZHANG Luming,XIA Yingjie,JI Rangping,et al.Spatial-aware object-level saliency prediction by learning graphlet hierarchies[J].IEEE Transactions on Industrial Electronics,2015,62(2):1301-1308.