Advertisement

MMW Image Blind Restoration Using Sparse ICA in Contourlet Transform Domain

  • Li Shang
  • Pin-gang Su
  • Wen-jun Huai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7389)

Abstract

Sparse independent component analysis (SPICA) algorithm is effective in blind separation of superimposed images, without having any priory knowledge about the image’s structure and statistics. While a millimeter wave (MMW) image contains the refective information of imaging object and much unknown noise of imaging scene, so the MMW image is too high blur to be discerned. To obtain preferable MMW image, combined the advantages of contourlet sparse transform and SPICA, a new blind restoration method proposed by us of MMW images operating in the contourlet sparse transform domain is discussed in this paper. Contourlet transform can retain the better contour of an image and make this image sparser in local subspace. Here, using the low frequency band and the high frequency bands of the first layer obtained by contourlet transform as the mixed input data of SPICA, the task of MMW image restoration can be implemented. In test, the blind restoration of mixed natural images is also operated by using our method, simultaneity, using the single noise ratio (SNR) to measure the restored natural images, experimental results testify the validity of our method in doing blind separation and it is feasible to restore the MMW image using this proposed method. Further, compared with methods of contourlet transform and fast ICA, simulations again show that this MMW image restoration method proposed is indeed efficient in application.

Keywords

Millimeter wave (MMW) Independent component analysis (ICA) Sparse ICA Contourlet sparse transform Image restoration 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Su, P., Wang, Z., Xu, Z.: Active MMW Focal Plane Imaging System. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS (LNAI), vol. 5755, pp. 875–881. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Sundareshan, M.K.: Bhattacharjee Supratik: Superresolution of Passive Millimeter-wave Images Using a Combined Maximum-likelihood Optimization and Projection-onto-convex-sets Approach. In: Proc. of SPIE Conf. on Passive Millimeter-wave Imaging Technology, Acrosense 2001, Orlando, FL, UAS, vol. 4373, pp. 105–116 (2001)Google Scholar
  3. 3.
    Cheng, P., Zhao, J.Q., Si, X.C., et al.: L-R Imaging Algorithm For Passive Millimeter Wave Based on Sparse Representation. Journal of Electronics & Information Technology 32, 1707–1711 (2010)Google Scholar
  4. 4.
    Hyvärinen, A., Karhunen, J.H., Oja, E., et al.: Independent Component Analysis. A Wiley-Interscience Publication, New York (1999)Google Scholar
  5. 5.
    Zhang, K., Chan, L.W.: ICA with Sparse Connections. Intelligent Data Engineering and Automated Learning 9, 530–537 (2006)Google Scholar
  6. 6.
    Hyvärinen, A.: Fast and Robust Fixed-point Algorithm for Independent Component Analysis. IEEE Transaction on Neural Networks 10, 626–634 (1999)CrossRefGoogle Scholar
  7. 7.
    Liu, S.S., Fang, Y.: A Contourlet-transform Based Sparse ICA Algorithm for Blind Image Separation. Journal of Shanghai University (English Edition) 11, 464–468 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Zibulevsky, M., Pearlmutter, B.A.: Blind Source Separation by Sparse Decomposition. Neural Computation 13, 863–882 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Bronstein, A.M., Bronstein, M.M., Zibulevsky, M., et al.: Sparse ICA for Blind Separation of Transmitted and Reflected Images. International Journal of Imaging Science and Technology 15, 84–91 (2005)CrossRefGoogle Scholar
  10. 10.
    Do, M., Vetterl, M.: The Contourlet Transform: An Efficient Directional Multiresolution Image Representation. IEEE Transactions on Image Processing 14, 2091–2106 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Li Shang
    • 1
    • 2
  • Pin-gang Su
    • 1
    • 3
  • Wen-jun Huai
    • 1
  1. 1.Department of Electronic Information EngineeringSuzhou Vocational UniversitySuzhouChina
  2. 2.Department of AutomationUniversity of Science and Technology of ChinaHefeiChina
  3. 3.State Key Lab of Millimeter WavesSoutheast UniversityNanjingChina

Personalised recommendations