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Hyperspectral Imagery Denoising Using a Spatial-Spectral Domain Mixing Prior

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Abstract

By introducing a novel spatial-spectral domain mixing prior, this paper establishes a Maximum a posteriori (MAP) framework for hyperspectral images (HSIs) denoising. The proposed mixing prior takes advantage of different properties of HSI in the spatial and spectral domain. Furthermore, we propose a spatially adaptive weighted prior combining smoothing prior and discontinuity-preserving prior in the spectral domain. The weights can be defined as a function of the spectral discontinuity measure (DM). For minimizing the objective function, a half-quadratic optimization algorithm is used. The experimental results illustrate that our proposed model can get a higher signal-to-noise ratio (SNR) than using only smoothing prior or discontinuity-preserving prior.

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References

  1. Letexier D, Bourennane S. Noise removal from hyperspectral images by multidimensional filtering. IEEE Trans. Geoscience and Remote Sensing, July 2008, 46(7): 2061–2069.

    Article  Google Scholar 

  2. Duarte-Carvajalino J M, Castillo P E, Velez-Reyes M. Comparative study of semi-implicit schemes for nonlinear diffusion in hyperspectral imagery. IEEE Trans. Image Processing, May 2007, 16(5): 1303–1314.

    Article  MathSciNet  Google Scholar 

  3. Martin-Herrero J. Anisotropic diffusion in the hypercube. IEEE Trans. Geoscience and Remote Sensing, May 2007, 45(5): 1386–1398.

    Article  Google Scholar 

  4. Shaw G A, Burke H K. Spectral imaging for remoting sensing. Lincoln Laboratory Journal, 2003, 14(1): 3–28.

    Google Scholar 

  5. Othman H, Qian S E. Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage. IEEE Trans. Geoscience and Remote Sensing, 2006, 44(2): 397–408.

    Article  Google Scholar 

  6. Atkinson I, Kamalabadi F, Jones D L. Wavelet-based hyperspectral image estimation. In Proc. IEEE International Geoscience and Remote Sensing Symposium, July 2003, pp.743–745.

  7. Krishnamurthy K, Willett R. Multiscale reconstruction of photon-limited hyperspectral data. In Proc. IEEE the 14th Workshop on Statistical Signal Processing, Aug. 2007, pp.596–600.

  8. Wang Y, Niu R, Yu X. Anisotropic diffusion for hyperspectral imagery enhancement. IEEE Sensors Journal, 2010, 10(3): 469–477.

    Article  Google Scholar 

  9. Karami A, Yazdi M, Asli A Z. Best rank-r tensor selection using genetic algorithm for better noise reduction and compression of hyperspectral images. In Proc. the 15th International Conference on Digital Information Management (ICDIM), July 2010, pp.169–173.

  10. Chen G, Qian S. Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans. Geoscience and Remote Sensing, 2011, 49(3): 973–980.

    Article  Google Scholar 

  11. Lennon M, Mercier G, Hubert-Moy L. Nonlinear filtering of hyperspectral images with anisotropic diffusion. In Proc. IEEE International Geoscience and Remote Sensing Symposium, June 2002, pp.2477–2479.

  12. Green A, Berman M, Switzer P, Craig M D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geoscience and Remote Sensing, 1988, 26(1): 65–74.

    Article  Google Scholar 

  13. Yuan Q, Zhang L, Shen H. Hyperspectral image denoising employing a spectral-spatial adaptive total variation model. IEEE Trans. Geoscience and Remote Sensing, 2012, to appear.

  14. Chen G, Bui T D, Krzyzak A. Denoising of three dimensional data cube using bivariate wavelet shrinking. In Lecture Notes in Computer Science 6111, Campilho A, Kamel M (Eds.), Springer Berlin/Heidelberg, 2010, pp.45–51.

  15. Geman S, Geman D. Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Analysis and Machine Intelligence, 1984, 6(6): 721–741.

    Article  MATH  Google Scholar 

  16. Schultz R R, Stevenson R L. A Bayesian approach to image expansion for improved definition. IEEE Trans. Image Processing, 1994, 3(3): 233–242.

    Article  Google Scholar 

  17. Hamza A B, Krim H. A variational approach to maximum a posteriori estimation for image denoising. In Lecture Notes in Computer Science 2134, Figueiredo M, Zerubia J, Jain A, (Eds.), Springer Berlin/Heidelberg, 2001, pp.19–33.

  18. Shen H, Zhang L. A Map-based algorithm for destriping and inpainting of remotely sensed images. IEEE Trans. Geoscience and Remote Sensing, 2009, 47(5): 1492–1502.

    Article  Google Scholar 

  19. Geman S, McClure D. Bayesian image analysis: An application to single photon emission tomography. In Proc. Statistical Computation Section, Washington DC: Amer, pp.12–18.

  20. Teboul S, Blanc-Feraud L, Aubert G, Barlaud M. Variational approach for edge-preserving regularization using coupled PDES, IEEE Trans. Image Processing, 1998, 7(3): 387–397.

    Article  Google Scholar 

  21. Charbonnier P, Blanc-Féraud L, Aubert C, Barlaud M. Deterministic edge-preserving regularization in computed imaging. IEEE Trans. Image Processing, 1997, 6(2):298–311.

    Article  Google Scholar 

  22. Geman D, Yang C. Nonlinear image recovery with halfquadratic regularization. IEEE Trans. Image Processing, 1995, 4(7): 932–946.

    Article  Google Scholar 

  23. Bauer F, Lukas M A. Comparing parameter choice methods for regularization of ill-posed problems. Mathematics and Computers in Simulation, 2011, 81(9): 1795–1841.

    Article  MathSciNet  MATH  Google Scholar 

  24. Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In Proc. the 16th International Conference on Computer Vision, Jan. 1998, pp.839–846.

  25. Tai S C, Yang S M. A fast method for image noise estimation using laplacian operator and adaptive edge detection. In Proc. the 3rd International Symposium on Communications, Control and Signal Processing, Mar. 2008, pp.1077–1081.

  26. Chen K. Adaptive smoothing via contextual and local discontinuities. IEEE Trans. Pattern Analysis and Machine Intelligence, 2005, 27(10): 1552–1567.

    Article  Google Scholar 

  27. Foster D, Nascimento S, Amano K. Information limits on neural identification of coloured surfaces in satural scenes. Visual Neuroscience, 2004, 21(3): 331–336.

    Article  Google Scholar 

  28. Cai S, Li K. Matlab implementation of wavelet transforms. http://eeweb.poly.edu/iselesni/WaveletSoftware/index.html, Feb. 2012.

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Authors and Affiliations

  1. National ASIC Design and Engineering Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Shao-Lin Chen, Xi-Yuan Hu (Member, IEEE) & Si-Long Peng

Authors
  1. Shao-Lin Chen
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  2. Xi-Yuan Hu
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  3. Si-Long Peng
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Corresponding author

Correspondence to Xi-Yuan Hu.

Additional information

This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 60972126, 60921061 and the State Key Program of National Natural Science of China under Grant No. 61032007.

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Chen, SL., Hu, XY. & Peng, SL. Hyperspectral Imagery Denoising Using a Spatial-Spectral Domain Mixing Prior. J. Comput. Sci. Technol. 27, 851–861 (2012). https://doi.org/10.1007/s11390-012-1269-1

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  • DOI: https://doi.org/10.1007/s11390-012-1269-1

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