Rapid Impulsive Noise Denoising in Range Images

  • Jian Wang
  • Zhen-qiang Yao
  • Lin Mei
  • Yao-jie Zhu
  • Yuan Yao
  • Yang Zhang
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 180)

Abstract

A novel rapid impulsive noise denoising method for range image is presented. In a 2D scan line acquired by a laser range finder, distribution features of impulsive noises (INs) are analyzed, and then a mathematical representation of the features is provided by defining a few new coefficients. Subsequently, a rule-based distinguishing criterion is formulated to detect two types of INs: dropouts and invaders. The traditional mean filter is improved by an automotive non-IN neighbor searching procedure. A compositive algorithm with a very low computational complexity has been implemented as an embedded module in our self-developed software with copyright. An experiment on real range image is performed, and the results indicate that the proposed method can detect all the impulsive noises accurately and denoise them with a significant efficiency. It is proven that the method is suitable for practical applications on industrial or other fields.

Keywords

Range image denoising Impulsive noises detestation and removal Computer vision Pattern recognition Laser radar scanning 

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References

  1. 1.
    Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation 4(2), 490–530 (2005)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  3. 3.
    Lindenbaum, M., Fischer, M., Bruckstein, A.: On Gabor’s contribution to image enhancement. Pattern Recognition 27(1), 1–8 (1994)CrossRefGoogle Scholar
  4. 4.
    Rudin, L.I., Osher, S.: Total variation based image restoration with free local constraints. In: Proceedings of IEEE International Conference on Image Processing, ICIP 1994, Austin, TX, USA, November 13-16, vol. 1, pp. 31–35. IEEE Press (1994)Google Scholar
  5. 5.
    Osher, S., Burger, M., Goldfarb, D., et al.: An iterative regularization method for total variation-based image restoration. Multiscale Modelling and Simulation 4(2), 460–489 (2005)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Yaroslavsky, L.P.: Digital picture processing: an introduction. In: Information Sciences. Springer, Berlin (1985)Google Scholar
  7. 7.
    Strela, V.: Denoising via block Wiener filtering in wavelet domain. In: Proceedings of the 3rd European Congress of Mathematics, Barcelona, Spain. Birkhäuser (July 2000)Google Scholar
  8. 8.
    Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Chipman, H.A., Kolaczyk, E.D., McCulloch, R.E.: Adaptive Bayesian wavelet shrinkage. Journal of the American Statistical Association 92(440), 1413–1421 (1997)MATHCrossRefGoogle Scholar
  10. 10.
    Jung, A.: An introduction to a new data analysis tool: Independent Component Analysis. In: Proceedings of Workshop GK Nonlinearity, Regensburg, Germany, March 18, pp. 1–16 (2002)Google Scholar
  11. 11.
    Qi, W., Qi, L., Zhe, C., et al.: Range image noise suppression in laser imaging system. Optics & Laser Technology 41(2), 140–147 (2009)CrossRefGoogle Scholar
  12. 12.
    Edeler, T., Ohliger, K., Hussmann, S.: Time-of-flight depth image denoising using prior noise information. In: Proceedings of 2010 10th International Conference on Signal Processing, ICSP, Beijing, China, October 24-28, pp. 119–122 (2010)Google Scholar
  13. 13.
    Wang, J., Yao, Z.-Q., An, Q.-Z., et al.: RIDED-2D: a rule-based instantaneous denoising and edge detection method for 2D range scan line. International Journal of Pattern Recognition and Artificial Intelligence 25(6), 807–833 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jian Wang
    • 1
    • 2
  • Zhen-qiang Yao
    • 2
  • Lin Mei
    • 1
  • Yao-jie Zhu
    • 3
  • Yuan Yao
    • 4
  • Yang Zhang
    • 5
  1. 1.Cyber Physical System R&D CenterThe Third Research Institute of Ministry of Public SecurityShanghaiP.R. China
  2. 2.State Key Laboratory of Mechanical System and VibrationShanghai Jiao Tong UniversityShanghaiP.R. China
  3. 3.Shanghai Yanfeng Visteon Automotive Trim Systems Co., Ltd.ShanghaiP.R. China
  4. 4.Information CenterNanjing University of Aeronautics and AstronauticsNanjingP.R. China
  5. 5.Clolrectal DepartmentNanjing Hospital of T.C.MNanjingP.R. China

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