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MMW Image Enhancement Based on Gray Stretch Technique and SSR Theory

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

Abstract

In order to improve the intensity and contrast qualities of Millimeter Wave (MMW) images and reduce the image noise for concealed weapons Detection, a method is proposed through combining the gray stretch technique and Retinex theory. Gray stretch is first used to preprocess the MMW image, and then Retinex theory based on Single-Scale Retinex (SSR) is used to suppress background clutters. As a result, both the image contrast and Peak Signal to Noise Ratio (PSNR) are improved efficiently. The simulation and experimental results have proved that the algorithm is not only effective in detecting MMW targets, but also has advantages of high speed and is easy for engineering applications.

Keywords

gray stretch technique MMW image Retinex theory illumination image reflectance image image enhancement 

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References

  1. 1.
    Roger, A.D., Gleed, G., Anderton, R.N.: Advances in Passive Millimetre Wave Imaging. In: Proceedings of SPIE, vol. 2211, pp. 312–317 (1994)Google Scholar
  2. 2.
    Wang, N.N., Qi, J.H., Deng, W.B.: Development Status of Millimeter Wave Imaging Systems for Concealed Detection. Infrared Technology 31(3), 129–135 (2009)zbMATHGoogle Scholar
  3. 3.
    Alan, L., Qi, H.H., Andrew, D.: An Overview of Recent Advances in Passive Millimetre Wave Imaging in the UK. In: Proceedings of SPIE, vol. 2744, pp. 146–153 (1996)Google Scholar
  4. 4.
    Wang, R.G., Zhu, J., Yang, W.T., et al.: An Improved Local Multi-scale Retinex Algorithm Based on Illuminance Image Segmentation. Tien Tzu Hsueh Pao/Acta Electronica Sinica 38(5), 1181–1186 (2010)Google Scholar
  5. 5.
    Bertalmio, M., Caselles, et al.: Issues about Retinex Theory And Contrast Enhancement. International Journal of Computer Vision 83(1), 101–119 (2009)CrossRefGoogle Scholar
  6. 6.
    Park, Y.K., Park, S.L., Kim, J.K.: Retinex Method Based on Adaptive Smoothing for Illumination Invariant Face Recognition. Signal Processing 88(8), 1929–1945 (2008)zbMATHCrossRefGoogle Scholar
  7. 7.
    Edwin, H.L.: An Alternative Technique for The Computation of The Designator in The Retinex Theory of Color Vision. Proc. Natl. Acad., Sci. USA 83, 3078–3080 (1986)CrossRefGoogle Scholar
  8. 8.
    Land, E.H.: The Retinex Theory of Color Vision. Scientific American 237(6), 108–128 (1977)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Kittler, J.: Minimum Error Thresholding. Pattern Recognition 19, 41–47 (1986)CrossRefGoogle Scholar
  10. 10.
    Zhang, X.J., Sun, X.L.: A Research on the Piecewise Linear Transformation in Adaptive IR Image Enhancement. Electronic Science and Technology 186(3), 13–16 (2005)Google Scholar
  11. 11.
    Sun, P.G., Wang, Z.X., Xu, Z.Y.: Active MMW Focal Plane Imaging System. Journal of Suzhou Vocational University 19(1), 70–73 (2008)MathSciNetGoogle Scholar
  12. 12.
    Shang, L., Su, P.G., Zhou, C.X.: Denoising Millimeter Wave Image Using Contourlet and Sparse Coding Shrinkage. Laser & Infrared 41(9), 1049–1053 (2011)Google Scholar
  13. 13.
    Huynh-Thu, Q., Ghanbari, M.: Scope of Validity of PSNR in Image/Video Quality Assessment. Electronics Letters 44(13), 800–801 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wen-Jun Huai
    • 1
  • Li Shang
    • 1
  • Pin-Gang Su
    • 1
  1. 1.Department of Electronic & Information EngineeringSuzhou Vocational UniversitySuzhouChina

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