Speckle Reduction in SAR Images by Using Homogeneity NeighShrink



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Submitted Oct 19, 2016
Published Jul 27, 2018
10.24018/ejeng.2016.1.4.176

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Synthetic Aperture Radar (SAR) image suffers from severe artifacts caused by speckle noise which is multiplicative in nature. Some of the adaptive filters such as the Lee filter, the Frost filter and the Kuan filter are the well known speckle filters. These filters adapt the filter coefficients based on the pixels within a fixed moving window. Though it removes speckle noise well in the homogeneous regions, it leaves noise in the heterogeneous areas to preserve the edges and fine details or smoothes the edges to remove the noise in this area. In order to reduce the speckle noise with the preservation of edges, a new method based on wavelet analysis and adaptive mean filtering is proposed in this paper. A comparative study with other methods show that proposed method is better in preserving edges and fine details while reducing the speckle noise.

Keywords: Speckle DWT (Discrete Wavelet Transform) Homogeneity Filter.

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References

  1. REFERENCES
     Google Scholar
  2. J.W.Goodman,(November 1976). Some properties of speckle, The Optical Society Of America, 66(11), pp 1145-1150.
     Google Scholar
  3. Iman Elyasi, Mohammad Ali Pourmina, (2016). Speckle Reduction In Breast Cancer Ultrasound Images By Using Homogeneity Modified Bayes Shrink, Elsevier’s Measurement.
     Google Scholar
  4. Alex F. de Araujo , Christos E. Constantinou, (October 2014). New Artificial Life Model For Image Enhancement, Elsevier’s Experts System With Applications. 41, pp. 5892- 5906.
     Google Scholar
  5. Jong Sen Lee, (January 2009). Improved Sigma Filter For Speckle Filtering Of SAR Imagery, IEEE Transaction On Geoscience And Remote Sensing, 47(1), pp. 202-213.
     Google Scholar
  6. Darwin T Kuan, (March 1987). Adaptive Restoration Of Images With Speckle. IEEE Transactions On Acoustics, Speech And Signal Processing, 35(3), pp. 373-383.
     Google Scholar
  7. V.S Frost, (January 1981). An Adaptive Filter For Smoothing Noisy Radar Images, Proceedings Of The IEEE, 69(1), pp. 133-136.
     Google Scholar
  8. Yongjian Yu and Scott T. Acton, (November 2002) Speckle Reducing Anisotropic Diffusion, IEEE Transactions On Image Processing, 11(11), pp. 1260-1270.
     Google Scholar
  9. Pierrick Coupe, (October 2009). Nonlocal Means-Based Speckle Filtering For Ultrasound Images, IEEE Transactions On Image Processing, 18(10), pp. 2221-2229.
     Google Scholar
  10. Y.Guo, Y Wang, (2011). Speckle Filtering Of Ultrasound Images Using A Modified Non Local-Based Algorithm, Elsevier’s Biomedical Signal Processing And Control, 6, pp. 129-138.
     Google Scholar
  11. Jian Yang, Jingfan Fan, (June 2016). Local Statistics and Non Local Mean Filter for Speckle Noise Reduction in Medical Ultrasound Image, Elsevier’s Neurocomputing, 195, pp. 88-95.
     Google Scholar
  12. D.L. Donoho, (May 1995). De-Noising By Soft Thresholding, IEEE Transactions On Information Theory, 41(3), pp. 613-627.
     Google Scholar
  13. G.Y. Chen, T.D. Bui and A.Krzyzak, (January 2005). Image Denoising With Neighbour Dependency And Customized Wavelet And Threshold, Pattern Recognition, 38(1), pp. 115-124.
     Google Scholar
  14. H. Guo, J.E. Odegard, I.W Selesnick, (1994). Wavelet Based Speckle Reduction With Application To SAR Based ATD/R, IEEE proceeding ICIP, 1, pp.75-79.
     Google Scholar
  15. Gregorio Andria and Filippo Attivissimo, (August 2013).A Suitable Threshold For Speckle Reduction In Ultrasound Images, IEEE Transactions On Instrumentation And Measurement, 62(8), pp. 2270-2278.
     Google Scholar
  16. S Gupta, R.C Chauhan, (February 2004). Wavelet Based Statistical Approach For Speckle Reduction In Medical Ultrasound Images, Springer’s Medical And Biological Engineering And Computing, 42(2), pp 189-192.
     Google Scholar
  17. Jong Sen Lee, (March 1980). Digital Image Enhancement And Noise Filtering By Use Of Local Statistics, IEEE Transactions On Pattern Analysis And Machine Learning, 2(2), pp 165-168.
     Google Scholar
  18. Jong Sen Lee, (1981). Speckle Analysis And Smoothing Of Synthetic Aperture Radar Images, Computer Graphics And Image Processing, 17, pp. 24-32.
     Google Scholar
  19. A. Achim, A. Bezerianos and P. Tsakalides, (August 2001). Novel Bayesian Multiscale Method For Speckle Removal In Medical Ultrasound Images, IEEE Transactions On Medicals Imaging, 20(8), pp. 772-783.
     Google Scholar
  20. I. Daubechies, (Philadelphia 1991). Ten Lectures On Wavelets, CMBS-NSF Regional Conference Series In Applied Mathematics.
     Google Scholar
  21. D.L Donoho, I.M. Johnstone, (1995). Wavelet Shrinkage: Asymptotic?, Journal Of The Royal Statistical Society: Series B, 57(2), pp 301-369.
     Google Scholar
  22. D.L. Donoho, I.M Johnstone, (1994). Ideal Spatial Adaption Via Wavelet Shrinkage, Biometrika, 81(3), pp. 425-455.
     Google Scholar
  23. D.L. Donoho, I.M. Johnstone, (1995). Adapting To Unknown Smoothness Via Wavelet Shrinkage, Journal Of American Statistical Association, 90(432), pp 1200-1224.
     Google Scholar
  24. Florian Luisier, Thierry Blu, Michael Unser, (2007).A New SURE Approach to Image Denoising : Interscale Orthonormal Wavelet Thresholding, IEEE Transactions On Image Processing, 16(3), pp.693-606.
     Google Scholar
  25. T.T. Cai and B.W. Silverman, (2001). Incorporating Information On Neighbouring Coefficients Into Wavelet Estimation, Sankhya: The Indian Journal Of Statistics, 63(B,2), pp. 127-148.
     Google Scholar
  26. Jong Sen Lee, (February 1983). A Simple Speckle Smoothing Algorithm For Synthetic Aperture Radar Images, IEEE Transactions On Systems, Man And Cybernetics, 13(1), pp. 85-89.
     Google Scholar
  27. Jong Sen Lee, (1981). Speckle Analysis And Smoothing Of Synthetic Aperture Radar Images, Computer Graphics And Image Processing, 17, pp 24-32.
     Google Scholar
  28. Achim A. and Bezerianos A, (August 2001). Novel Bayesian Multiscale Method For Speckle Removal In Medical Ultrasound Images, IEEE Transactions On Medical Imaging, 20(8), pp 772- 783.
     Google Scholar
  29. A. Lopes, R. Touzi, and E. Nezry, (November 1990). Adaptive Speckle Filters And Scene Heterogeneity, IEEE Transactions On Geoscience And Remote Sensing, 28(6), page 992-1000.
     Google Scholar
  30. Zhou Wang, Alan C. Bovik, H.R. Sheikh, (April 2004). Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions On Image Processing, 13(4), pp. 1-14.
     Google Scholar