New Wavelet-Based Superresolution Algorithm for Speckle Reduction in SAR Images
This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
[1] H.S. Tan. (2001, October). Denoising of Noise Speckle in Radar Image.
(Online). Available:
http://innovexpo.itee.uq.edu.au/2001/projects/s804294/thesis.pdf
[2] 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.
[3] 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.
[4] S.G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for
image denoising and compression," IEEE Transactions on Image
Processing, vol. 9, no. 9, pp.1532-1546, September 2000.
[5] X.-P. Zhang, "Thresholding Neural Network for Adaptive Noise
reduction," IEEE Transactions on Neural Networks, vol.12, no. 3,
pp.567-584, May 2001.
[6] N. K. Bose and S. Lertrattanapanich. Advances in wavelet
superresolution. (Online). Available:
http://www.personal.psu.edu/users/s/x/sxl46/Bose01w.pdf
[7] S. P. Kim and N. K. Bose, "Reconstruction of 2-D bandlimited discrete
signals from nonuniform samples," IEE Proceedings, Vol.137, No.3, Part
F, June 1990, pp. 197-203.
[8] Seunghyeon Rhee and Moon Gi Kang, "Discrete cosine transform based
regularized high-resolution image reconstruction algorithm," Optical
Engineering, Vol.38, No.8, 1999, pp. 1348-1356.
[9] S. P. Kim, N. K. Bose and H. M. Valenzuela, "Recursive reconstruction
of high resolution image from noisy undersampled multiframes," IEEE
Trans. on Acoust., Speech, and Signal Process., Vol.38, 1990, pp. 1013-
1027.
[10] C. Ford and D. Etter, "Wavelet basis reconstruction of nonuniformly
sampled data," IEEE Trans. Circuits and System II, Vol.45, No.8,
August 1998, pp.1165-1168.
[11] N. Nguyen and P. Milanfar, "A wavelet-based interpolation-restoration
method for supperresolution (wavelet superresolution)," Circuits
Systems and Signal Process, Vol.19, No.4, 2000, pp.321-338.
[12] F. Argenti and L. Alparone, "Speckle removal from SAR images in the
undecimated wavelet domain," IEEE Trans. Geosci. Remote Sensing,
vol. 40, pp. 2363-2374, Nov. 2002.
[13] H. Xie, L. E. Pierce, and F. T. Ulaby, "Statistical properties of
logarithmically transformed speckle," IEEE Trans. Geosci. Remote
Sensing, vol. 40, pp. 721-727, Mar. 2002.
[14] J.W. Goodman, "Some fundamental properties of speckle," Journal
Optics Society of America, 66:1145-1150, 1976.
[15] S.J. Leon, Linear Algebra with Applications, Maxwell Macmillan
International Editions, New York, 1990.
[16] G.G. Walter, "Sampling Theorems and Wavelets," in Handbook of
Statistics, Vol. 10, (eds. N.K. Bose and C.R. Rao), Elsevier Science
Publishers B.V., North-Holland, Amsterdam, The Netherlands, 1993, pp.
883-903.
[17] M. Vetterli and J. Kovacevic, "Wavelets and Subband Coding," Prentice
Hall PTR, Upper Saddle River, New Jersey 07458, 1995.
[18] S. Mann and R.W. Picard, "Video orbits of the projective group: A
simple approach to featureless estimation of parameters," IEEE
Transactions on Image Processing, Vol. 6, No.9, September 1997,
pp.1281-1295.
[19] S. Lertrattanapanich and N.K. Bose, "Latest results on high-resolution
reconstruction from video sequences," Technical Report of IEICE,
DSP99-140, The Institution of Electronic, Information and
Communication Engineers, Japan, December 1999, pp.59-65.
[20] R. Willett, R. Nowak, I. Jermyn, and J. Zerubia. Wavelet-Based
Superresolution in Astronomy Astronomical Data Analysis Software and
Systems XIII, ASP Conference Series, vol. 314, pp.107-116, 2004.
(Online). Available: http://www.adass.org/adass/proceedings/
adass03/reprints/O2-1.pdf
[21] N.X. Nguyen. (2000, July). Numerical Algorithms for Image
Superresolution. Ph.D. thesis, Stanford University. (Online). Available:
http://www.cse.ucsc.edu/~milanfar/NguyenPhDThesis.ps
[22] D.L. Ward. (2003, March). Redundant discrete wavelet transform based
super-resolution using sub-pixel image registration. M.S. thesis,
Department of the Air Force, Air University, Air Force Institute of
Technology, Wright-Patterson Air Force Base, Ohio. (Online).
Available: https://research.maxwell.af.mil/ papers/ay2003/afit/AFIT-GEENG--
03-18.pdf
[23] S. Borman. (2004, April). Topics in multiframe superresolution
restoration,"Ph.D. thesis, University of Notre Dame, Indiana. (Online).
Available: http://www.seanborman.com/publications/BormanPhD.pdf
[24] F.M. Candocia. (1998, May). A unified superresolution approach for
optical and Synthetic Aperture Radar images. Ph.D. thesis, University of
Florida. (Online). Available:
http://www.cnel.ufl.edu/bib/pdf_dissertation/ candocia_dissertation.pdf
[25] F. Xiao, G. Wang, and Z. Xu, "Superresolution in two-color excitation
fluorescence microscopy," Optics Communications, vol.228, pp. 225-
230, 2003.
[26] Y. Yu, and S.T. Acton, "Speckle Reducing Anisotropic Diffusion," IEEE
Trans. on Image Process-ing, vol. 11, no. 11, pp.1260-1270, 2002.
[27] M. Mastriani and A. Giraldez, "Enhanced Directional Smoothing
Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar
Images," Journal of Measurement Science Review, vol 4, no. 3, pp.1-11,
2004. (Online). Available:
http://www.measurement.sk/2004/S3/Mastriani.pdf
[1] H.S. Tan. (2001, October). Denoising of Noise Speckle in Radar Image.
(Online). Available:
http://innovexpo.itee.uq.edu.au/2001/projects/s804294/thesis.pdf
[2] 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.
[3] 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.
[4] S.G. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for
image denoising and compression," IEEE Transactions on Image
Processing, vol. 9, no. 9, pp.1532-1546, September 2000.
[5] X.-P. Zhang, "Thresholding Neural Network for Adaptive Noise
reduction," IEEE Transactions on Neural Networks, vol.12, no. 3,
pp.567-584, May 2001.
[6] N. K. Bose and S. Lertrattanapanich. Advances in wavelet
superresolution. (Online). Available:
http://www.personal.psu.edu/users/s/x/sxl46/Bose01w.pdf
[7] S. P. Kim and N. K. Bose, "Reconstruction of 2-D bandlimited discrete
signals from nonuniform samples," IEE Proceedings, Vol.137, No.3, Part
F, June 1990, pp. 197-203.
[8] Seunghyeon Rhee and Moon Gi Kang, "Discrete cosine transform based
regularized high-resolution image reconstruction algorithm," Optical
Engineering, Vol.38, No.8, 1999, pp. 1348-1356.
[9] S. P. Kim, N. K. Bose and H. M. Valenzuela, "Recursive reconstruction
of high resolution image from noisy undersampled multiframes," IEEE
Trans. on Acoust., Speech, and Signal Process., Vol.38, 1990, pp. 1013-
1027.
[10] C. Ford and D. Etter, "Wavelet basis reconstruction of nonuniformly
sampled data," IEEE Trans. Circuits and System II, Vol.45, No.8,
August 1998, pp.1165-1168.
[11] N. Nguyen and P. Milanfar, "A wavelet-based interpolation-restoration
method for supperresolution (wavelet superresolution)," Circuits
Systems and Signal Process, Vol.19, No.4, 2000, pp.321-338.
[12] F. Argenti and L. Alparone, "Speckle removal from SAR images in the
undecimated wavelet domain," IEEE Trans. Geosci. Remote Sensing,
vol. 40, pp. 2363-2374, Nov. 2002.
[13] H. Xie, L. E. Pierce, and F. T. Ulaby, "Statistical properties of
logarithmically transformed speckle," IEEE Trans. Geosci. Remote
Sensing, vol. 40, pp. 721-727, Mar. 2002.
[14] J.W. Goodman, "Some fundamental properties of speckle," Journal
Optics Society of America, 66:1145-1150, 1976.
[15] S.J. Leon, Linear Algebra with Applications, Maxwell Macmillan
International Editions, New York, 1990.
[16] G.G. Walter, "Sampling Theorems and Wavelets," in Handbook of
Statistics, Vol. 10, (eds. N.K. Bose and C.R. Rao), Elsevier Science
Publishers B.V., North-Holland, Amsterdam, The Netherlands, 1993, pp.
883-903.
[17] M. Vetterli and J. Kovacevic, "Wavelets and Subband Coding," Prentice
Hall PTR, Upper Saddle River, New Jersey 07458, 1995.
[18] S. Mann and R.W. Picard, "Video orbits of the projective group: A
simple approach to featureless estimation of parameters," IEEE
Transactions on Image Processing, Vol. 6, No.9, September 1997,
pp.1281-1295.
[19] S. Lertrattanapanich and N.K. Bose, "Latest results on high-resolution
reconstruction from video sequences," Technical Report of IEICE,
DSP99-140, The Institution of Electronic, Information and
Communication Engineers, Japan, December 1999, pp.59-65.
[20] R. Willett, R. Nowak, I. Jermyn, and J. Zerubia. Wavelet-Based
Superresolution in Astronomy Astronomical Data Analysis Software and
Systems XIII, ASP Conference Series, vol. 314, pp.107-116, 2004.
(Online). Available: http://www.adass.org/adass/proceedings/
adass03/reprints/O2-1.pdf
[21] N.X. Nguyen. (2000, July). Numerical Algorithms for Image
Superresolution. Ph.D. thesis, Stanford University. (Online). Available:
http://www.cse.ucsc.edu/~milanfar/NguyenPhDThesis.ps
[22] D.L. Ward. (2003, March). Redundant discrete wavelet transform based
super-resolution using sub-pixel image registration. M.S. thesis,
Department of the Air Force, Air University, Air Force Institute of
Technology, Wright-Patterson Air Force Base, Ohio. (Online).
Available: https://research.maxwell.af.mil/ papers/ay2003/afit/AFIT-GEENG--
03-18.pdf
[23] S. Borman. (2004, April). Topics in multiframe superresolution
restoration,"Ph.D. thesis, University of Notre Dame, Indiana. (Online).
Available: http://www.seanborman.com/publications/BormanPhD.pdf
[24] F.M. Candocia. (1998, May). A unified superresolution approach for
optical and Synthetic Aperture Radar images. Ph.D. thesis, University of
Florida. (Online). Available:
http://www.cnel.ufl.edu/bib/pdf_dissertation/ candocia_dissertation.pdf
[25] F. Xiao, G. Wang, and Z. Xu, "Superresolution in two-color excitation
fluorescence microscopy," Optics Communications, vol.228, pp. 225-
230, 2003.
[26] Y. Yu, and S.T. Acton, "Speckle Reducing Anisotropic Diffusion," IEEE
Trans. on Image Process-ing, vol. 11, no. 11, pp.1260-1270, 2002.
[27] M. Mastriani and A. Giraldez, "Enhanced Directional Smoothing
Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar
Images," Journal of Measurement Science Review, vol 4, no. 3, pp.1-11,
2004. (Online). Available:
http://www.measurement.sk/2004/S3/Mastriani.pdf
@article{"International Journal of Information, Control and Computer Sciences:53871", author = "Mario Mastriani", title = "New Wavelet-Based Superresolution Algorithm for Speckle Reduction in SAR Images", abstract = "This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.
", keywords = "Projection, speckle, superresolution, synthetic
aperture radar, thresholding, wavelets.", volume = "2", number = "3", pages = "735-8", }
