Despeckling of Synthetic Aperture Radar Images Using Inner Product Spaces in Undecimated Wavelet Domain
This paper introduces the effective speckle reduction of
synthetic aperture radar (SAR) images using inner product spaces in
undecimated wavelet domain. There are two major areas in projection
onto span algorithm where improvement can be made. First is the use
of undecimated wavelet transformation instead of discrete wavelet
transformation. And second area is the use of smoothing filter namely
directional smoothing filter which is an additional step. Proposed
method does not need any noise estimation and thresholding
technique. More over proposed method gives good results on both
single polarimetric and fully polarimetric SAR images.
[1] Mario Mastriani, "New Wavelet-Based Super resolution Algorithm for
Speckle Reduction in SAR Images". International journal of computer
science (IJCS), Vol 1, ISSN 1306-4428-2006, pp.291-298, 2006.
[2] Saevarsson, B.B, Sveinsson, J.R, Benediktsson J.A, "Combined Wavelet
and Curvelet Denoising of SAR Images", IEEE Transactions on
Geosciences and Remote Sensing, Vol 6, pp. 4235-4238, Sept 2004.
[3] Guozhong Chen, Xingzhao Liu "Wavelet-Based Despeckling SAR
Images Using Neighbouring Wavelet Coefficients" IEEE conference on
RADAR, Vol 3,pp.212-216,2005.
[4] Guozhong Chen, Xingzhao Liu, "An Improved Wavelet -Based Method
for SAR image Denoising Using Data Fusion" IEEE conference on
RADAR, Vol. 6, pp. 818-822, April 2006.
[5] D. Lee Fugal , "Conceptual Wavelets in Digital Signal Processing", 2007.
[6] M. I. H. Bhuiyan, M. Omair Ahmed M. N. S. Swamy, "Wavelet-based
Spatially Adaptive Method for Despeckling SAR Images", IEEE
International Symposium on Circuits and Systems, ISCAS, Vol. 10,
pp.1109-1112, May 2006.
[7] Sheng Guofang ,Hu Xin , Jiao Licheng, "SAR Image Denoising based on
Data Fusion", Proceedings of the fifth computational intelligence and
multimedia applications ICCIMA, Vol 10, pp.143-148,Sept 2003.
[8] Aglika Gyaourova "Undecimated Wavelet Transforms for Image
Denoising" center for applied scientific computing, Lawrence Livermore
nation laboratory, November 19, 2002.
[9] D.L .Donoho, "Denoising by soft Thresholding," IEEE Trans. on Inform
Theory, vol. 41, pp. 613-627, May 1995.
[10] H.S. Tan, "Denoising of Noise Speckle in Radar Image", Oct 2001.
[11] H. Guo, J.E. Odegard, M. Lang, R.A. Gopinath, I. Selesnick, and
C.S.Burrus, "Speckle reduction via wavelet shrinkage with application to
SAR based ATD/R," Technical Report CML TR94-02, CML, Rice
University, Houston, 1994.
[12] D.L. Donoho and I.M. Johnstone, "Adapting to unknown smoothness via
wavelet shrinkage," Journal of the American Statistical Association, vol.
90, no. 432, pp. 1200-1224, 1995.
[13] S.G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for
image denoising and compression," IEEE Transactions on Images
Processing, vol 9, no. 9, pp.1532-1546, September 2000.
[14] X. P. Zhang, "Thresholding Neural Network for Adaptive Noise
reduction," IEEE Transactions on Neural Networks, vol.12, no.3,
pp567-584.May 2001.
[15] S. M. Yusuf, Abdul Majeed, Muhammad Amin "Mathematical Methods"
2000.
[16] Anil K. Jain "Fundamentals of Digital Image Processing.2004.
[1] Mario Mastriani, "New Wavelet-Based Super resolution Algorithm for
Speckle Reduction in SAR Images". International journal of computer
science (IJCS), Vol 1, ISSN 1306-4428-2006, pp.291-298, 2006.
[2] Saevarsson, B.B, Sveinsson, J.R, Benediktsson J.A, "Combined Wavelet
and Curvelet Denoising of SAR Images", IEEE Transactions on
Geosciences and Remote Sensing, Vol 6, pp. 4235-4238, Sept 2004.
[3] Guozhong Chen, Xingzhao Liu "Wavelet-Based Despeckling SAR
Images Using Neighbouring Wavelet Coefficients" IEEE conference on
RADAR, Vol 3,pp.212-216,2005.
[4] Guozhong Chen, Xingzhao Liu, "An Improved Wavelet -Based Method
for SAR image Denoising Using Data Fusion" IEEE conference on
RADAR, Vol. 6, pp. 818-822, April 2006.
[5] D. Lee Fugal , "Conceptual Wavelets in Digital Signal Processing", 2007.
[6] M. I. H. Bhuiyan, M. Omair Ahmed M. N. S. Swamy, "Wavelet-based
Spatially Adaptive Method for Despeckling SAR Images", IEEE
International Symposium on Circuits and Systems, ISCAS, Vol. 10,
pp.1109-1112, May 2006.
[7] Sheng Guofang ,Hu Xin , Jiao Licheng, "SAR Image Denoising based on
Data Fusion", Proceedings of the fifth computational intelligence and
multimedia applications ICCIMA, Vol 10, pp.143-148,Sept 2003.
[8] Aglika Gyaourova "Undecimated Wavelet Transforms for Image
Denoising" center for applied scientific computing, Lawrence Livermore
nation laboratory, November 19, 2002.
[9] D.L .Donoho, "Denoising by soft Thresholding," IEEE Trans. on Inform
Theory, vol. 41, pp. 613-627, May 1995.
[10] H.S. Tan, "Denoising of Noise Speckle in Radar Image", Oct 2001.
[11] H. Guo, J.E. Odegard, M. Lang, R.A. Gopinath, I. Selesnick, and
C.S.Burrus, "Speckle reduction via wavelet shrinkage with application to
SAR based ATD/R," Technical Report CML TR94-02, CML, Rice
University, Houston, 1994.
[12] D.L. Donoho and I.M. Johnstone, "Adapting to unknown smoothness via
wavelet shrinkage," Journal of the American Statistical Association, vol.
90, no. 432, pp. 1200-1224, 1995.
[13] S.G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for
image denoising and compression," IEEE Transactions on Images
Processing, vol 9, no. 9, pp.1532-1546, September 2000.
[14] X. P. Zhang, "Thresholding Neural Network for Adaptive Noise
reduction," IEEE Transactions on Neural Networks, vol.12, no.3,
pp567-584.May 2001.
[15] S. M. Yusuf, Abdul Majeed, Muhammad Amin "Mathematical Methods"
2000.
[16] Anil K. Jain "Fundamentals of Digital Image Processing.2004.
@article{"International Journal of Information, Control and Computer Sciences:63404", author = "Syed Musharaf Ali and Muhammad Younus Javed and Naveed Sarfraz Khattak and Athar Mohsin and UmarFarooq", title = "Despeckling of Synthetic Aperture Radar Images Using Inner Product Spaces in Undecimated Wavelet Domain", abstract = "This paper introduces the effective speckle reduction of
synthetic aperture radar (SAR) images using inner product spaces in
undecimated wavelet domain. There are two major areas in projection
onto span algorithm where improvement can be made. First is the use
of undecimated wavelet transformation instead of discrete wavelet
transformation. And second area is the use of smoothing filter namely
directional smoothing filter which is an additional step. Proposed
method does not need any noise estimation and thresholding
technique. More over proposed method gives good results on both
single polarimetric and fully polarimetric SAR images.", keywords = "Directional Smoothing, Inner product, Length ofvector, Undecimated wavelet transformation.", volume = "2", number = "1", pages = "206-6", }
