Research Papers

A Three-Dimensional Diffusion Filtering Model Establishment and Analysis for Point Cloud Intensity Noise

[+] Author and Article Information
Yi Zhang

School of Geodesy and Geomatics,
Wuhan University,
Wuhan, Hubei 430072, China
e-mail: yzhang@sgg.whu.edu.cn

Xiuqin Lyu

School of Resource and
Environmental Science,
Wuhan University,
Wuhan, Hubei 430072, China
e-mail: winterlxq@sina.com

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 29, 2016; final manuscript received October 14, 2016; published online November 16, 2016. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(1), 011010 (Nov 16, 2016) (5 pages) Paper No: JCISE-16-1871; doi: 10.1115/1.4035000 History: Received February 29, 2016; Revised October 14, 2016

To improve the quality of point cloud data, as well as maintain edge and detail information in the course of filtering intensity data, a three-dimensional (3D) diffusion filtering equation based on the general principle of diffusion filtering is established in this paper. Moreover, we derive theoretical formulas for the scale parameter and maximum iteration number and achieve self-adaptive denoising, fine control of the point cloud filtering, and accurate prediction of the diffusion convergence. Through experiments with three types of typical point cloud intensity data, the theoretical formulas for the scale parameter and iteration number are verified. Comparative experiments with point cloud data of different types show that the 3D diffusion filtering method has significant denoising and edge-preserving abilities. Compared with the traditional median filtering algorithm, the signal-to-noise ratio (SNR) of the point cloud after filtering is increased by an average of 10% and above, with a maximum value of 40% and above.

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Li, Z. , Li, Q. , and Wang, Q. , 2004, “ Noise Characteristic in Active Laser Imaging System by Statistic Analysis,” Chin. J. Lasers, 31(9), pp. 1081–1085.
Chen, Y. , Shen, Y. , Jianbing, J. I. , and Guojun, L. I. , 2003, “ A New Fast Weighted Median Filtering Algorithm,” Comput. Eng., 29(3), pp. 89–90.
Li, X. , Jiang, D. , and Wang, Z. , 2012, “ Weighted Median Filtering of Im Based on Grads Similarity,” J. Univ. Electron. Sci. Technol. China, 41(1), pp. 114–119.
Feng, X. K. , Xiao, X. M. , and Yin, H. J. , 2000, “ The Directional Medial Filtering With Weights,” J. Image Graphics, 5A(7), pp. 609–611.
Czerwinski, R. N. , Jones, D. L. , and O'Brien, W. D. , 1995, “ Ultrasound Speckle Reduction by Directional Median Filtering,” International Conference on Image Processing, Vol. 1, pp. 358–358.
Shu Tao, L. I. , and Wang, Y. N. , 2000, “ Non-Linear Adaptive Removal of Salt and Pepper Noise From Images,” J. Image Graphics, 5A(12), pp. 999–1001.
Zhao, C. H. , Hui, J. Y. , Wang, W. , and Sun, S. H. , 2000, “ A Class of Adaptive Ranked-Order Morphological Filters,” J. Image Graphics, 5A(8), pp. 674–677.
Ma, J. , Yan, J. B. , Liu, G. Z. , and Liu, J. X. , 2010, “ Anisotropic Diffusion Smoothing of Mixed Noise,” J. Cent. South Univ., 41(1), pp. 231–237.
Bai, J. , and Feng, X. C. , 2007, “ Fractional-Order Anisotropic Diffusion for Image Denoising,” IEEE Trans. Image Process., 16(10), pp. 2492–2502. [CrossRef] [PubMed]
Chen, S. , and Yang, X. , 2006, “ A New Adaptive Diffusion Equation for Image Noise Removal and Feature Preservation,” International Conference on Pattern Recognition, Vol. 3, pp. 885–888.
Zhang, C. , Zhao, Y. , and Lian, Y. , 2012, “ Research on Anisotropic Diffusion Denoising Development,” Electron. Opt. Control, 19(5), pp. 51–55.
Qian, S. , Zhu, J. , Zhiwei, L. I. , Yin, H. , and Bo, H. U. , 2009, “ A New Adaptive InSAR Interferogram Filter Based on SNR,” Acta Geod. Cartographica Sin., 38(5), pp. 437–442.
Yang, S. B. , Bing Bai, L. I. , Shen, S. H. , and Zhang, P. P. , 2006, “ Structure Retaining Linear Multi-Channel SAR Image Speckle Filter,” Acta Geod. Cartographica Sin., 35(4), pp. 364–370.
Jiang, L. , Zhao, C. , and Wang, Q. , 2003, “ Algorithm About Suppressing Speckle Noise in Coherent Laser Radar Imagery,” Acta Opt. Sin., 23(5), pp. 541–546.
Jiang, L. , and Zhao, C. , 2003, “ Speckle Noise Suppressing Based on Multilevel Nonlinear Weighted Mean Median Filter,” Laser Infrared, 33(5), pp. 380–382.
Li, Z. Q. , Wang, Q. , and Qi, L. I. , 2003, “ Comparison of Algorithms for Suppressing Speckle in Laser Imaging System,” Infrared Laser Eng., 32(4), pp. 130–133.
Lai, X. D. , and Wan, Y. C. , 2005, “ A Kind of Filtering Algorithms for Lidar Intensity Image Based on Flatness Terrain,” Chin. J. Lasers, 32(10), pp. 1325–1329.
Nobrega, R. A. A. , Quintanilha, J. A. , and O'Hara, C. G. , 2007, “ A Noise-Removal Approach for Lidar Intensity Images Using Anisotropic Diffusion Filtering to Preserve Object Shape Characteristics,” ASPRS Annual Conference, Tampa, FL, May 7–11.
Perona, P. , and Malik, J. , 1990, “ Scale-Space and Edge Detection Using Anisotropic Diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., 12(7), pp. 629–639. [CrossRef]
Zou, M. , 2004, Deconvolution and Signal Recovery, National Defence Industry Press, Beijing, China, pp. 186–188.
Wang, X. , Wang, C. , and Zhang, Y. , 2010, “ Research on SNR of Point Target Image,” Electron. Opt. Control, 17(1), pp. 18–21.


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Fig. 1

Three types of typical point cloud intensity data: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 2

Theoretical distribution of the scale parameter k=d2/λε in the neighborhood: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 3

Theoretical distribution of the iteration number t<1.5+kλε/2d2 in the neighborhood: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 4

Change curves of the three types of typical point cloud data: (a) intensity gradient, (b) salt-and-pepper noise, and (c) intensity edge

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Fig. 5

Comparison of various point clouds after 3D diffusion and median filtering (k = 1.0)

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Fig. 6

SNR curves of various point clouds after 3D diffusion filtering




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