Abstract

Speckle noise is a multiplicative type of noise commonly seen in medical and remote sensing images. It gives a granular appearance that degrades the quality of the recorded images. These speckle noise components need to be mitigated before the image is used for further processing and analysis. This paper presents a novel approach for removing granular speckle noise in gray scale images. We used an efficient multiscale image representation scheme named fast multiscale directional filter bank (FMDFB) along with simple threshold methods such as Vishushrink for image processing. It is a perfect reconstruction framework that can be used for a wide range of image processing applications because of its directionality and reduced computational complexity. The FMDFB-based speckle mitigation is appealing over other traditional multiscale approaches such as wavelets and Contourlets. Our experimental results show that the despeckling performance of the proposed method outperforms the wavelet and Contourlet-based despeckling methods.

© 2014 Optical Society of America

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    [CrossRef]
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  8. A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: a review,” Phys. Chem. Commun. 3, 1–18 (2010).
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  24. P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
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  25. M. N. Do and M. Vetterli, “Framing pyramids,” IEEE Trans. Signal Process. 51, 2329–2342 (2003).
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  26. R. H. Bamberger and M. J. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40, 882–893 (1992).
    [CrossRef]
  27. S. Sutha, E. J. Leavline, and D. A. A. Gnana Sing, “IHNS: a pragmatic investigation on identifying highly noisy subband in FMDFB for fixing threshold to deteriorate noise in images,” Inf. Technol. J. 12, 1289–1298 (2013).
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    [CrossRef]
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  32. L. Gang, L. Xutao, L. Xiaogeng, and F. Bo, “An adaptive denoising and enhancing algorithm based on the MAP rule in the contourlet domain for infrared image,” in International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), pp. 1–5.
  33. Z.-F. Zhou and P.-L. Shui, “Contourlet-based image denoising algorithm using directional windows,” Electron. Lett. 43, 92–93 (2007).
    [CrossRef]
  34. Z. Dan, X. Chen, H. Gan, and C. Gao, “Locally adaptive shearlet denoising based on bayesian MAP estimate,” in Sixth International Conference on Image and Graphics (ICIG, 2011), pp. 28–32.
  35. G. G. Bhutada, R. S. Anand, and S. C. Saxena, “Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform,” Digital Signal Process. 21, 118–130 (2011).
    [CrossRef]
  36. G. Y. Chen and B. Kégl, “Image denoising with complex ridgelets,” Pattern Recogn. 40, 578–585 (2007).
    [CrossRef]
  37. L. Shang, P. Su, and T. Liu, “Denoising MMW image using the combination method of contourlet and KSC shrinkage,” Neurocomputing 83, 229–233 (2012).
    [CrossRef]
  38. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Gatesmark, 2009), Vol. 2.
  39. Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (IEEE, 2003), Vol. 2, pp. 1398–1402.
  40. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
    [CrossRef]
  41. L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
    [CrossRef]

2013 (3)

E. J. Leavline and D. A. A. G. Singh, “Enhanced modified decision based unsymmetric trimmed median filter for salt and pepper noise removal,” Int. J. Imaging Robot. 11, 46–56 (2013).

S. Sutha, E. J. Leavline, and D. A. A. G. Singh, “A comprehensive study on wavelet based shrinkage methods for denoising natural images,” WSEAS Trans. Signal Process. 9, 203–215 (2013).

S. Sutha, E. J. Leavline, and D. A. A. Gnana Sing, “IHNS: a pragmatic investigation on identifying highly noisy subband in FMDFB for fixing threshold to deteriorate noise in images,” Inf. Technol. J. 12, 1289–1298 (2013).

2012 (2)

L. Shang, P. Su, and T. Liu, “Denoising MMW image using the combination method of contourlet and KSC shrinkage,” Neurocomputing 83, 229–233 (2012).
[CrossRef]

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

2011 (6)

E. Bae, J. Shi, and X.-C. Tai, “Graph cuts for curvature based image denoising,” IEEE Trans. Image Process. 20, 1199–1210 (2011).
[CrossRef]

A. M. Atto, D. Pastor, and G. Mercier, “Wavelet shrinkage: unification of basic thresholding functions and thresholds,” Signal Image Video Process. 5, 11–28 (2011).

E. J. Leavline, S. Sutha, and D. A. A. G. Singh, “Wavelet domain shrinkage methods for noise removal in images. A compendium,” Int. J. Comput. Appl. Technol. 33, 28–32 (2011).

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

H. X. Huang, J. Gong, and T. Zhang, “Method of adaptive wavelet thresholding used in image denoising,” Adv. Mater. Res 204, 1184–1187 (2011).
[CrossRef]

G. G. Bhutada, R. S. Anand, and S. C. Saxena, “Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform,” Digital Signal Process. 21, 118–130 (2011).
[CrossRef]

2010 (2)

M. Forouzanfar, H. A. Moghaddam, and M. Gity, “A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images,” Signal Image Video Process. 4, 359–375 (2010).

A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: a review,” Phys. Chem. Commun. 3, 1–18 (2010).
[CrossRef]

2009 (4)

D. Cho, T. D. Bui, and G. Chen, “Image denoising based on wavelet shrinkage using neighbor and level dependency,” Int. J. Wavelets Multires. Inf. Process. 7, 299–311 (2009).

H. Yu, L. Zhao, and H. Wang, “Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain,” IEEE Trans. Image Process. 18, 2364–2369 (2009).
[CrossRef]

M. Nasri and H. Nezamabadi-pour, “Image denoising in the wavelet domain using a new adaptive thresholding function,” Neurocomputing 72, 1012–1025 (2009).
[CrossRef]

L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras,” IEEE Trans. Image Process. 18, 797–812 (2009).
[CrossRef]

2007 (7)

S. Mallat and G. Peyré, “A review of bandlet methods for geometrical image representation,” Numer. Algorithms 44, 205–234 (2007).
[CrossRef]

K. Guo and D. Labate, “Optimally sparse multidimensional representation using shearlets,” SIAM J. Math. Anal. 39, 298–318 (2007).
[CrossRef]

G. Y. Chen and B. Kégl, “Image denoising with complex ridgelets,” Pattern Recogn. 40, 578–585 (2007).
[CrossRef]

Z.-F. Zhou and P.-L. Shui, “Contourlet-based image denoising algorithm using directional windows,” Electron. Lett. 43, 92–93 (2007).
[CrossRef]

S. Sudha, G. R. Suresh, and R. Sukanesh, “Wavelet based image denoising using adaptive subband thresholding,” IJSC 2, 628–632 (2007).

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “Multiscale directional filter bank with applications to structured and random texture retrieval,” Pattern Recogn. 40, 1182–1194 (2007).
[CrossRef]

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “A novel fast and reduced redundancy structure for multiscale directional filter banks,” IEEE Trans. Image Process. 16, 2058–2068 (2007).
[CrossRef]

2006 (1)

V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, and P. L. Dragotti, “Directionlets: anisotropic multidirectional representation with separable filtering,” IEEE Trans. Image Process. 15, 1916–1933 (2006).
[CrossRef]

2005 (1)

M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Process. 14, 2091–2106 (2005).
[CrossRef]

2004 (1)

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

2003 (1)

M. N. Do and M. Vetterli, “Framing pyramids,” IEEE Trans. Signal Process. 51, 2329–2342 (2003).
[CrossRef]

2002 (1)

J.-L. Starck, E. J. Candès, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[CrossRef]

1997 (1)

F. G. Meyer and R. R. Coifman, “Brushlets: a tool for directional image analysis and image compression,” Appl. Comput. Harmon. Anal. 4, 147–187 (1997).
[CrossRef]

1996 (1)

T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (1996).
[CrossRef]

1995 (1)

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41, 613–627 (1995).
[CrossRef]

1992 (1)

R. H. Bamberger and M. J. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40, 882–893 (1992).
[CrossRef]

1983 (1)

P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

Adelson, E.

P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

Akansu, A. N.

A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: a review,” Phys. Chem. Commun. 3, 1–18 (2010).
[CrossRef]

Anand, R. S.

G. G. Bhutada, R. S. Anand, and S. C. Saxena, “Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform,” Digital Signal Process. 21, 118–130 (2011).
[CrossRef]

Andria, G.

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

Attivissimo, F.

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

Atto, A. M.

A. M. Atto, D. Pastor, and G. Mercier, “Wavelet shrinkage: unification of basic thresholding functions and thresholds,” Signal Image Video Process. 5, 11–28 (2011).

Bae, E.

E. Bae, J. Shi, and X.-C. Tai, “Graph cuts for curvature based image denoising,” IEEE Trans. Image Process. 20, 1199–1210 (2011).
[CrossRef]

Bamberger, R. H.

R. H. Bamberger and M. J. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40, 882–893 (1992).
[CrossRef]

Beferull-Lozano, B.

V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, and P. L. Dragotti, “Directionlets: anisotropic multidirectional representation with separable filtering,” IEEE Trans. Image Process. 15, 1916–1933 (2006).
[CrossRef]

Bhutada, G. G.

G. G. Bhutada, R. S. Anand, and S. C. Saxena, “Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform,” Digital Signal Process. 21, 118–130 (2011).
[CrossRef]

Bo, F.

L. Gang, L. Xutao, L. Xiaogeng, and F. Bo, “An adaptive denoising and enhancing algorithm based on the MAP rule in the contourlet domain for infrared image,” in International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), pp. 1–5.

Bovik, A. C.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (IEEE, 2003), Vol. 2, pp. 1398–1402.

Bui, T. D.

D. Cho, T. D. Bui, and G. Chen, “Image denoising based on wavelet shrinkage using neighbor and level dependency,” Int. J. Wavelets Multires. Inf. Process. 7, 299–311 (2009).

Burt, P.

P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

Candès, E. J.

J.-L. Starck, E. J. Candès, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[CrossRef]

Cavone, G.

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

Chen, G.

D. Cho, T. D. Bui, and G. Chen, “Image denoising based on wavelet shrinkage using neighbor and level dependency,” Int. J. Wavelets Multires. Inf. Process. 7, 299–311 (2009).

Chen, G. Y.

G. Y. Chen and B. Kégl, “Image denoising with complex ridgelets,” Pattern Recogn. 40, 578–585 (2007).
[CrossRef]

Chen, X.

Z. Dan, X. Chen, H. Gan, and C. Gao, “Locally adaptive shearlet denoising based on bayesian MAP estimate,” in Sixth International Conference on Image and Graphics (ICIG, 2011), pp. 28–32.

Cheng, K.-O.

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “A novel fast and reduced redundancy structure for multiscale directional filter banks,” IEEE Trans. Image Process. 16, 2058–2068 (2007).
[CrossRef]

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “Multiscale directional filter bank with applications to structured and random texture retrieval,” Pattern Recogn. 40, 1182–1194 (2007).
[CrossRef]

Cho, D.

D. Cho, T. D. Bui, and G. Chen, “Image denoising based on wavelet shrinkage using neighbor and level dependency,” Int. J. Wavelets Multires. Inf. Process. 7, 299–311 (2009).

Coifman, R. R.

F. G. Meyer and R. R. Coifman, “Brushlets: a tool for directional image analysis and image compression,” Appl. Comput. Harmon. Anal. 4, 147–187 (1997).
[CrossRef]

Dan, Z.

Z. Dan, X. Chen, H. Gan, and C. Gao, “Locally adaptive shearlet denoising based on bayesian MAP estimate,” in Sixth International Conference on Image and Graphics (ICIG, 2011), pp. 28–32.

Deng, W.

J. Yang, R. Feng, and W. Deng, “A new algorithm of image denoising based on stationary wavelet multi-scale adaptive threshold,” in 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT, 2011), Vol. 9, pp. 4550–4553.

Do, M. N.

M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Process. 14, 2091–2106 (2005).
[CrossRef]

M. N. Do and M. Vetterli, “Framing pyramids,” IEEE Trans. Signal Process. 51, 2329–2342 (2003).
[CrossRef]

M. N. Do and M. Vetterli, “Pyramidal directional filter banks and curvelets,” in Proceedings of the 2001 International Conference on Image Processing (IEEE, 2001), Vol. 3, pp. 158–161.

M. N. Do, Directional Multiresolution Image Representations (Citeseer, 2001), Vol. 2500.

Donoho, D. L.

J.-L. Starck, E. J. Candès, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[CrossRef]

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41, 613–627 (1995).
[CrossRef]

Dragotti, P. L.

V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, and P. L. Dragotti, “Directionlets: anisotropic multidirectional representation with separable filtering,” IEEE Trans. Image Process. 15, 1916–1933 (2006).
[CrossRef]

Eddins, S. L.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Gatesmark, 2009), Vol. 2.

Feng, R.

J. Yang, R. Feng, and W. Deng, “A new algorithm of image denoising based on stationary wavelet multi-scale adaptive threshold,” in 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT, 2011), Vol. 9, pp. 4550–4553.

Forouzanfar, M.

M. Forouzanfar, H. A. Moghaddam, and M. Gity, “A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images,” Signal Image Video Process. 4, 359–375 (2010).

Gan, H.

Z. Dan, X. Chen, H. Gan, and C. Gao, “Locally adaptive shearlet denoising based on bayesian MAP estimate,” in Sixth International Conference on Image and Graphics (ICIG, 2011), pp. 28–32.

Gang, L.

L. Gang, L. Xutao, L. Xiaogeng, and F. Bo, “An adaptive denoising and enhancing algorithm based on the MAP rule in the contourlet domain for infrared image,” in International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), pp. 1–5.

Gao, C.

Z. Dan, X. Chen, H. Gan, and C. Gao, “Locally adaptive shearlet denoising based on bayesian MAP estimate,” in Sixth International Conference on Image and Graphics (ICIG, 2011), pp. 28–32.

Giaquinto, N.

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

Gity, M.

M. Forouzanfar, H. A. Moghaddam, and M. Gity, “A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images,” Signal Image Video Process. 4, 359–375 (2010).

Gnana Sing, D. A. A.

S. Sutha, E. J. Leavline, and D. A. A. Gnana Sing, “IHNS: a pragmatic investigation on identifying highly noisy subband in FMDFB for fixing threshold to deteriorate noise in images,” Inf. Technol. J. 12, 1289–1298 (2013).

Gong, J.

H. X. Huang, J. Gong, and T. Zhang, “Method of adaptive wavelet thresholding used in image denoising,” Adv. Mater. Res 204, 1184–1187 (2011).
[CrossRef]

Gonzalez, R. C.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Gatesmark, 2009), Vol. 2.

Guo, K.

K. Guo and D. Labate, “Optimally sparse multidimensional representation using shearlets,” SIAM J. Math. Anal. 39, 298–318 (2007).
[CrossRef]

Huang, H. X.

H. X. Huang, J. Gong, and T. Zhang, “Method of adaptive wavelet thresholding used in image denoising,” Adv. Mater. Res 204, 1184–1187 (2011).
[CrossRef]

Kégl, B.

G. Y. Chen and B. Kégl, “Image denoising with complex ridgelets,” Pattern Recogn. 40, 578–585 (2007).
[CrossRef]

Labate, D.

K. Guo and D. Labate, “Optimally sparse multidimensional representation using shearlets,” SIAM J. Math. Anal. 39, 298–318 (2007).
[CrossRef]

Lanzolla, A. M. L.

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

Law, N.-F.

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “A novel fast and reduced redundancy structure for multiscale directional filter banks,” IEEE Trans. Image Process. 16, 2058–2068 (2007).
[CrossRef]

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “Multiscale directional filter bank with applications to structured and random texture retrieval,” Pattern Recogn. 40, 1182–1194 (2007).
[CrossRef]

Leavline, E. J.

E. J. Leavline and D. A. A. G. Singh, “Enhanced modified decision based unsymmetric trimmed median filter for salt and pepper noise removal,” Int. J. Imaging Robot. 11, 46–56 (2013).

S. Sutha, E. J. Leavline, and D. A. A. Gnana Sing, “IHNS: a pragmatic investigation on identifying highly noisy subband in FMDFB for fixing threshold to deteriorate noise in images,” Inf. Technol. J. 12, 1289–1298 (2013).

S. Sutha, E. J. Leavline, and D. A. A. G. Singh, “A comprehensive study on wavelet based shrinkage methods for denoising natural images,” WSEAS Trans. Signal Process. 9, 203–215 (2013).

E. J. Leavline, S. Sutha, and D. A. A. G. Singh, “Wavelet domain shrinkage methods for noise removal in images. A compendium,” Int. J. Comput. Appl. Technol. 33, 28–32 (2011).

Lee, T. S.

T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (1996).
[CrossRef]

Liu, T.

L. Shang, P. Su, and T. Liu, “Denoising MMW image using the combination method of contourlet and KSC shrinkage,” Neurocomputing 83, 229–233 (2012).
[CrossRef]

Lukac, R.

L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras,” IEEE Trans. Image Process. 18, 797–812 (2009).
[CrossRef]

Mallat, S.

S. Mallat and G. Peyré, “A review of bandlet methods for geometrical image representation,” Numer. Algorithms 44, 205–234 (2007).
[CrossRef]

Mercier, G.

A. M. Atto, D. Pastor, and G. Mercier, “Wavelet shrinkage: unification of basic thresholding functions and thresholds,” Signal Image Video Process. 5, 11–28 (2011).

Meyer, F. G.

F. G. Meyer and R. R. Coifman, “Brushlets: a tool for directional image analysis and image compression,” Appl. Comput. Harmon. Anal. 4, 147–187 (1997).
[CrossRef]

Moghaddam, H. A.

M. Forouzanfar, H. A. Moghaddam, and M. Gity, “A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images,” Signal Image Video Process. 4, 359–375 (2010).

Mou, X.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

Nasri, M.

M. Nasri and H. Nezamabadi-pour, “Image denoising in the wavelet domain using a new adaptive thresholding function,” Neurocomputing 72, 1012–1025 (2009).
[CrossRef]

Nezamabadi-pour, H.

M. Nasri and H. Nezamabadi-pour, “Image denoising in the wavelet domain using a new adaptive thresholding function,” Neurocomputing 72, 1012–1025 (2009).
[CrossRef]

Pastor, D.

A. M. Atto, D. Pastor, and G. Mercier, “Wavelet shrinkage: unification of basic thresholding functions and thresholds,” Signal Image Video Process. 5, 11–28 (2011).

Peyré, G.

S. Mallat and G. Peyré, “A review of bandlet methods for geometrical image representation,” Numer. Algorithms 44, 205–234 (2007).
[CrossRef]

Saxena, S. C.

G. G. Bhutada, R. S. Anand, and S. C. Saxena, “Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform,” Digital Signal Process. 21, 118–130 (2011).
[CrossRef]

Selesnick, I. W.

A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: a review,” Phys. Chem. Commun. 3, 1–18 (2010).
[CrossRef]

Serdijn, W. A.

A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: a review,” Phys. Chem. Commun. 3, 1–18 (2010).
[CrossRef]

Shang, L.

L. Shang, P. Su, and T. Liu, “Denoising MMW image using the combination method of contourlet and KSC shrinkage,” Neurocomputing 83, 229–233 (2012).
[CrossRef]

Sheikh, H. R.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

Shi, J.

E. Bae, J. Shi, and X.-C. Tai, “Graph cuts for curvature based image denoising,” IEEE Trans. Image Process. 20, 1199–1210 (2011).
[CrossRef]

Shui, P.-L.

Z.-F. Zhou and P.-L. Shui, “Contourlet-based image denoising algorithm using directional windows,” Electron. Lett. 43, 92–93 (2007).
[CrossRef]

Simoncelli, E. P.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (IEEE, 2003), Vol. 2, pp. 1398–1402.

Singh, D. A. A. G.

E. J. Leavline and D. A. A. G. Singh, “Enhanced modified decision based unsymmetric trimmed median filter for salt and pepper noise removal,” Int. J. Imaging Robot. 11, 46–56 (2013).

S. Sutha, E. J. Leavline, and D. A. A. G. Singh, “A comprehensive study on wavelet based shrinkage methods for denoising natural images,” WSEAS Trans. Signal Process. 9, 203–215 (2013).

E. J. Leavline, S. Sutha, and D. A. A. G. Singh, “Wavelet domain shrinkage methods for noise removal in images. A compendium,” Int. J. Comput. Appl. Technol. 33, 28–32 (2011).

Siu, W.-C.

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “Multiscale directional filter bank with applications to structured and random texture retrieval,” Pattern Recogn. 40, 1182–1194 (2007).
[CrossRef]

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “A novel fast and reduced redundancy structure for multiscale directional filter banks,” IEEE Trans. Image Process. 16, 2058–2068 (2007).
[CrossRef]

Smith, M. J.

R. H. Bamberger and M. J. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40, 882–893 (1992).
[CrossRef]

Starck, J.-L.

J.-L. Starck, E. J. Candès, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[CrossRef]

Su, P.

L. Shang, P. Su, and T. Liu, “Denoising MMW image using the combination method of contourlet and KSC shrinkage,” Neurocomputing 83, 229–233 (2012).
[CrossRef]

Sudha, S.

S. Sudha, G. R. Suresh, and R. Sukanesh, “Wavelet based image denoising using adaptive subband thresholding,” IJSC 2, 628–632 (2007).

Sukanesh, R.

S. Sudha, G. R. Suresh, and R. Sukanesh, “Wavelet based image denoising using adaptive subband thresholding,” IJSC 2, 628–632 (2007).

Suresh, G. R.

S. Sudha, G. R. Suresh, and R. Sukanesh, “Wavelet based image denoising using adaptive subband thresholding,” IJSC 2, 628–632 (2007).

Sutha, S.

S. Sutha, E. J. Leavline, and D. A. A. Gnana Sing, “IHNS: a pragmatic investigation on identifying highly noisy subband in FMDFB for fixing threshold to deteriorate noise in images,” Inf. Technol. J. 12, 1289–1298 (2013).

S. Sutha, E. J. Leavline, and D. A. A. G. Singh, “A comprehensive study on wavelet based shrinkage methods for denoising natural images,” WSEAS Trans. Signal Process. 9, 203–215 (2013).

E. J. Leavline, S. Sutha, and D. A. A. G. Singh, “Wavelet domain shrinkage methods for noise removal in images. A compendium,” Int. J. Comput. Appl. Technol. 33, 28–32 (2011).

Tai, X.-C.

E. Bae, J. Shi, and X.-C. Tai, “Graph cuts for curvature based image denoising,” IEEE Trans. Image Process. 20, 1199–1210 (2011).
[CrossRef]

Velisavljevic, V.

V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, and P. L. Dragotti, “Directionlets: anisotropic multidirectional representation with separable filtering,” IEEE Trans. Image Process. 15, 1916–1933 (2006).
[CrossRef]

Vetterli, M.

V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, and P. L. Dragotti, “Directionlets: anisotropic multidirectional representation with separable filtering,” IEEE Trans. Image Process. 15, 1916–1933 (2006).
[CrossRef]

M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Process. 14, 2091–2106 (2005).
[CrossRef]

M. N. Do and M. Vetterli, “Framing pyramids,” IEEE Trans. Signal Process. 51, 2329–2342 (2003).
[CrossRef]

M. N. Do and M. Vetterli, “Pyramidal directional filter banks and curvelets,” in Proceedings of the 2001 International Conference on Image Processing (IEEE, 2001), Vol. 3, pp. 158–161.

Wang, H.

H. Yu, L. Zhao, and H. Wang, “Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain,” IEEE Trans. Image Process. 18, 2364–2369 (2009).
[CrossRef]

Wang, Z.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (IEEE, 2003), Vol. 2, pp. 1398–1402.

Woods, R. E.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Gatesmark, 2009), Vol. 2.

Wu, X.

L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras,” IEEE Trans. Image Process. 18, 797–812 (2009).
[CrossRef]

Xiaogeng, L.

L. Gang, L. Xutao, L. Xiaogeng, and F. Bo, “An adaptive denoising and enhancing algorithm based on the MAP rule in the contourlet domain for infrared image,” in International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), pp. 1–5.

Xutao, L.

L. Gang, L. Xutao, L. Xiaogeng, and F. Bo, “An adaptive denoising and enhancing algorithm based on the MAP rule in the contourlet domain for infrared image,” in International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), pp. 1–5.

Yang, J.

J. Yang, R. Feng, and W. Deng, “A new algorithm of image denoising based on stationary wavelet multi-scale adaptive threshold,” in 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT, 2011), Vol. 9, pp. 4550–4553.

Yu, H.

H. Yu, L. Zhao, and H. Wang, “Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain,” IEEE Trans. Image Process. 18, 2364–2369 (2009).
[CrossRef]

Zhang, D.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras,” IEEE Trans. Image Process. 18, 797–812 (2009).
[CrossRef]

Zhang, L.

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras,” IEEE Trans. Image Process. 18, 797–812 (2009).
[CrossRef]

Zhang, T.

H. X. Huang, J. Gong, and T. Zhang, “Method of adaptive wavelet thresholding used in image denoising,” Adv. Mater. Res 204, 1184–1187 (2011).
[CrossRef]

Zhao, L.

H. Yu, L. Zhao, and H. Wang, “Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain,” IEEE Trans. Image Process. 18, 2364–2369 (2009).
[CrossRef]

Zhou, Z.-F.

Z.-F. Zhou and P.-L. Shui, “Contourlet-based image denoising algorithm using directional windows,” Electron. Lett. 43, 92–93 (2007).
[CrossRef]

Adv. Mater. Res (1)

H. X. Huang, J. Gong, and T. Zhang, “Method of adaptive wavelet thresholding used in image denoising,” Adv. Mater. Res 204, 1184–1187 (2011).
[CrossRef]

Appl. Comput. Harmon. Anal. (1)

F. G. Meyer and R. R. Coifman, “Brushlets: a tool for directional image analysis and image compression,” Appl. Comput. Harmon. Anal. 4, 147–187 (1997).
[CrossRef]

Digital Signal Process. (1)

G. G. Bhutada, R. S. Anand, and S. C. Saxena, “Edge preserved image enhancement using adaptive fusion of images denoised by wavelet and curvelet transform,” Digital Signal Process. 21, 118–130 (2011).
[CrossRef]

Electron. Lett. (1)

Z.-F. Zhou and P.-L. Shui, “Contourlet-based image denoising algorithm using directional windows,” Electron. Lett. 43, 92–93 (2007).
[CrossRef]

IEEE Trans. Commun. (1)

P. Burt and E. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31, 532–540 (1983).
[CrossRef]

IEEE Trans. Image Process. (9)

V. Velisavljevic, B. Beferull-Lozano, M. Vetterli, and P. L. Dragotti, “Directionlets: anisotropic multidirectional representation with separable filtering,” IEEE Trans. Image Process. 15, 1916–1933 (2006).
[CrossRef]

J.-L. Starck, E. J. Candès, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Trans. Image Process. 11, 670–684 (2002).
[CrossRef]

M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Trans. Image Process. 14, 2091–2106 (2005).
[CrossRef]

L. Zhang, R. Lukac, X. Wu, and D. Zhang, “PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras,” IEEE Trans. Image Process. 18, 797–812 (2009).
[CrossRef]

E. Bae, J. Shi, and X.-C. Tai, “Graph cuts for curvature based image denoising,” IEEE Trans. Image Process. 20, 1199–1210 (2011).
[CrossRef]

H. Yu, L. Zhao, and H. Wang, “Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain,” IEEE Trans. Image Process. 18, 2364–2369 (2009).
[CrossRef]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600–612 (2004).
[CrossRef]

L. Zhang, L. Zhang, X. Mou, and D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Trans. Image Process. 20, 2378–2386 (2011).
[CrossRef]

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “A novel fast and reduced redundancy structure for multiscale directional filter banks,” IEEE Trans. Image Process. 16, 2058–2068 (2007).
[CrossRef]

IEEE Trans. Inf. Theory (1)

D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory 41, 613–627 (1995).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Trans. Pattern Anal. Mach. Intell. 18, 959–971 (1996).
[CrossRef]

IEEE Trans. Signal Process. (2)

M. N. Do and M. Vetterli, “Framing pyramids,” IEEE Trans. Signal Process. 51, 2329–2342 (2003).
[CrossRef]

R. H. Bamberger and M. J. Smith, “A filter bank for the directional decomposition of images: theory and design,” IEEE Trans. Signal Process. 40, 882–893 (1992).
[CrossRef]

IJSC (1)

S. Sudha, G. R. Suresh, and R. Sukanesh, “Wavelet based image denoising using adaptive subband thresholding,” IJSC 2, 628–632 (2007).

Inf. Technol. J. (1)

S. Sutha, E. J. Leavline, and D. A. A. Gnana Sing, “IHNS: a pragmatic investigation on identifying highly noisy subband in FMDFB for fixing threshold to deteriorate noise in images,” Inf. Technol. J. 12, 1289–1298 (2013).

Int. J. Comput. Appl. Technol. (1)

E. J. Leavline, S. Sutha, and D. A. A. G. Singh, “Wavelet domain shrinkage methods for noise removal in images. A compendium,” Int. J. Comput. Appl. Technol. 33, 28–32 (2011).

Int. J. Imaging Robot. (1)

E. J. Leavline and D. A. A. G. Singh, “Enhanced modified decision based unsymmetric trimmed median filter for salt and pepper noise removal,” Int. J. Imaging Robot. 11, 46–56 (2013).

Int. J. Wavelets Multires. Inf. Process. (1)

D. Cho, T. D. Bui, and G. Chen, “Image denoising based on wavelet shrinkage using neighbor and level dependency,” Int. J. Wavelets Multires. Inf. Process. 7, 299–311 (2009).

Measurement (1)

G. Andria, F. Attivissimo, G. Cavone, N. Giaquinto, and A. M. L. Lanzolla, “Linear filtering of 2-D wavelet coefficients for denoising ultrasound medical images,” Measurement 45, 1792–1800 (2012).
[CrossRef]

Neurocomputing (2)

M. Nasri and H. Nezamabadi-pour, “Image denoising in the wavelet domain using a new adaptive thresholding function,” Neurocomputing 72, 1012–1025 (2009).
[CrossRef]

L. Shang, P. Su, and T. Liu, “Denoising MMW image using the combination method of contourlet and KSC shrinkage,” Neurocomputing 83, 229–233 (2012).
[CrossRef]

Numer. Algorithms (1)

S. Mallat and G. Peyré, “A review of bandlet methods for geometrical image representation,” Numer. Algorithms 44, 205–234 (2007).
[CrossRef]

Pattern Recogn. (2)

K.-O. Cheng, N.-F. Law, and W.-C. Siu, “Multiscale directional filter bank with applications to structured and random texture retrieval,” Pattern Recogn. 40, 1182–1194 (2007).
[CrossRef]

G. Y. Chen and B. Kégl, “Image denoising with complex ridgelets,” Pattern Recogn. 40, 578–585 (2007).
[CrossRef]

Phys. Chem. Commun. (1)

A. N. Akansu, W. A. Serdijn, and I. W. Selesnick, “Emerging applications of wavelets: a review,” Phys. Chem. Commun. 3, 1–18 (2010).
[CrossRef]

SIAM J. Math. Anal. (1)

K. Guo and D. Labate, “Optimally sparse multidimensional representation using shearlets,” SIAM J. Math. Anal. 39, 298–318 (2007).
[CrossRef]

Signal Image Video Process. (2)

A. M. Atto, D. Pastor, and G. Mercier, “Wavelet shrinkage: unification of basic thresholding functions and thresholds,” Signal Image Video Process. 5, 11–28 (2011).

M. Forouzanfar, H. A. Moghaddam, and M. Gity, “A new multiscale Bayesian algorithm for speckle reduction in medical ultrasound images,” Signal Image Video Process. 4, 359–375 (2010).

WSEAS Trans. Signal Process. (1)

S. Sutha, E. J. Leavline, and D. A. A. G. Singh, “A comprehensive study on wavelet based shrinkage methods for denoising natural images,” WSEAS Trans. Signal Process. 9, 203–215 (2013).

Other (7)

M. N. Do and M. Vetterli, “Pyramidal directional filter banks and curvelets,” in Proceedings of the 2001 International Conference on Image Processing (IEEE, 2001), Vol. 3, pp. 158–161.

M. N. Do, Directional Multiresolution Image Representations (Citeseer, 2001), Vol. 2500.

Z. Dan, X. Chen, H. Gan, and C. Gao, “Locally adaptive shearlet denoising based on bayesian MAP estimate,” in Sixth International Conference on Image and Graphics (ICIG, 2011), pp. 28–32.

J. Yang, R. Feng, and W. Deng, “A new algorithm of image denoising based on stationary wavelet multi-scale adaptive threshold,” in 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT, 2011), Vol. 9, pp. 4550–4553.

L. Gang, L. Xutao, L. Xiaogeng, and F. Bo, “An adaptive denoising and enhancing algorithm based on the MAP rule in the contourlet domain for infrared image,” in International Conference on Computational Intelligence and Software Engineering (CiSE, 2009), pp. 1–5.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB (Gatesmark, 2009), Vol. 2.

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers (IEEE, 2003), Vol. 2, pp. 1398–1402.

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Figures (12)

Fig. 1.
Fig. 1.

Iterative double filter bank structure for Contourlet transform.

Fig. 2.
Fig. 2.

Analysis and synthesis filter bank structure for FMDFB. hL and gL are the lowpass and highpass filters, respectively, in LP. Bi and Bi1 represent the back sampling and inverse back sampling. DFB is the directional filter bank for direction decomposition [23].

Fig. 3.
Fig. 3.

Proposed speckle mitigation method using FMDFB.

Fig. 4.
Fig. 4.

Comparison of MSE, PSNR, SSIM, and FSIM for wavelet, Contourlet, and the proposed despeckling methods on standard gray scale images with a simulated speckle noise variance of 0.01.

Fig. 5.
Fig. 5.

Comparison of wavelet, Contourlet, and the proposed despeckling methods on standard gray scale images with a simulated speckle noise variance of 0.01.

Fig. 6.
Fig. 6.

Comparison of wavelet, Contourlet, and the proposed despeckling methods on real ultrasound images of floating gallstones with a simulated speckle noise variance of 0.001.

Fig. 7.
Fig. 7.

Comparison of wavelet, Contourlet, and the proposed despeckling methods on real ultrasound images of floating gallstones (49954-Afbeelding8) with a simulated speckle noise variance of 0.001.

Fig. 8.
Fig. 8.

Comparison of wavelet, Contourlet, and the proposed despeckling methods on real ultrasound images of mobile gallstones (47214-Afbeelding2) with simulated a speckle noise variance of 0.001.

Fig. 9.
Fig. 9.

Comparison of SSIM and FSIM for wavelet, Contourlet, and the proposed despeckling methods on real ultrasound images of rolling and floating gallstones with various simulated speckle noise variance.

Fig. 10.
Fig. 10.

Comparison of the average time taken for computation of wavelet, Contourlet, and the proposed despeckling method.

Fig. 11.
Fig. 11.

Comparison of SSIM for wavelet, Contourlet, and the proposed FMDFB despeckling method and FMDFB despeckling method with NSSA on real ultrasound images of rolling and floating gallstones with a speckle noise variance 0.001.

Fig. 12.
Fig. 12.

Comparison of despeckled images using wavelet, Contourlet, and the proposed FMDFB despeckling method and FMDFB despeckling method with NSSA on real ultrasound image of floating gallstones (49950-Afbeelding4) with a speckle noise variance 0.001.

Tables (3)

Tables Icon

Table 1. MSE, PSNR, SSIM, and FSIM of Wavelet, Contourlet, and the Proposed Despeckling Method on Standard Gray Scale Images with a Simulated Speckle Noise Variance of 0.01

Tables Icon

Table 2. MSE, PSNR, SSIM, and FSIM of Wavelet, Contourlet, and the Proposed Despeckling Method on Real Ultrasound Images of Floating Gallstones with a Simulated Speckle Noise Variance of 0.01

Tables Icon

Table 3. Average Time Taken for Computation of Wavelet, Contourlet, and the Proposed Despeckling Method

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

MSE=1MNX=1MY=1N(F(X,Y)I(X,Y))2.
PSNR=20log10(Fmax/MSE)
lM(x,y)=2μxμy+C1μx2+μy2+C1,
cj(x,y)=2σxσy+C2σx2+σy2+C2,
sj(x,y)=σxy+C3σxσy+C3.
SSIM(x,y)=[lM(x,y)]αM·j=1M[cj(x,y)]βj[sj(x,y)]γj.

Metrics