Abstract

A novel image fusion algorithm based on bidimensional empirical mode decomposition (BEMD) applied to multi-focus color microscopic images is proposed in this paper. The fusion scheme is implemented in YIQ color model, aiming at achieving a balanced result between local feature enhancement and global tonality rendition. In the proposed algorithm, image decomposition is performed on luminance component by BEMD which can perform fully two-dimensional decomposition adaptively without using a priori basis. Upon fusion of each IMF component, the local significance principle fusion rule is used. When fusing the Residue component, the principal component analysis method is adopted. Thanks to the superior quality of BEMD in extracting salient features, the proposed algorithm can gain better fusion results not only in aspect of in-focus information extraction but also in performance of blur elimination. Experimental results demonstrate that the proposed algorithm outperforms the popular fusion algorithm based on wavelet transform. The usage of different color models for realization of the proposed algorithm is also discussed, and YIQ color model is proved to be more suitable.

© 2010 OSA

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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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  20. T. Zaveri and M. Zaveri, “A Novel Two Step Region Based Multifocus Image Fusion Method,” Int. J. Comput. Electr. Eng. 2, 86–91 (2010).
  21. B. Li and H. Lv, “Pixel level image fusion scheme based on accumulated gradient and PCA transform,” J. Commun. Comput. 6, 49–54 (2009).
  22. S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput. 26(7), 971–979 (2008).
    [CrossRef]

2010 (2)

J. Yang, C. Liu, and L. Zhang, “Color space normalization: Enhancing the discriminating power of color spaces for face recognition,” Pattern Recognit. 43(4), 1454–1466 (2010).
[CrossRef]

T. Zaveri and M. Zaveri, “A Novel Two Step Region Based Multifocus Image Fusion Method,” Int. J. Comput. Electr. Eng. 2, 86–91 (2010).

2009 (4)

B. Li and H. Lv, “Pixel level image fusion scheme based on accumulated gradient and PCA transform,” J. Commun. Comput. 6, 49–54 (2009).

S. Equis and P. Jacquot, “The empirical mode decomposition: a must-have tool in speckle interferometry?” Opt. Express 17(2), 611–623 (2009), http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-17-2-611 .
[CrossRef] [PubMed]

Q. Yin, L. Shen, J. N. Kim, and Y. J. Jeong, “Scale-invariant pattern recognition using a combined Mellin radial harmonic function and the bidimensional empirical mode decomposition,” Opt. Express 17(19), 16581–16589 (2009), http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-17-19-16581 .
[CrossRef] [PubMed]

P. V. Alfonso, T. A. Irwing, T. Q. Carina, and C. Santiago-Tepantlan, “Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies Wavelet family,” Proc. SPIE 7443, 744327 (2009).

2008 (4)

Z. Liu and C. Liu, “Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition,” Comput. Vis. Image Underst. 111(3), 249–262 (2008).
[CrossRef]

H. Zhao, Q. Li, and H. Feng, “Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map,” Image Vis. Comput. 26(9), 1285–1295 (2008).
[CrossRef]

S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput. 26(7), 971–979 (2008).
[CrossRef]

S. Yazdanfar, K. B. Kenny, K. Tasimi, A. D. Corwin, E. L. Dixon, and R. J. Filkins, “Simple and robust image-based autofocusing for digital microscopy,” Opt. Express 16(12), 8670–8677 (2008), http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-16-12-8670 .
[CrossRef] [PubMed]

2005 (2)

2003 (1)

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

2001 (3)

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognit. 34(12), 2259–2281 (2001).
[CrossRef]

L. Bogoni and M. Hansen, “Pattern-selective color image fusion,” Pattern Recognit. 34(8), 1515–1526 (2001).
[CrossRef]

Q. Guihong, Z. Dali, and Y. Pingfan, “Medical image fusion by wavelet transform modulus maxima,” Opt. Express 9(4), 184–190 (2001), http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-9-4-184 .
[CrossRef] [PubMed]

1998 (1)

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

1983 (1)

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[CrossRef]

Adelson, E. H.

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[CrossRef]

Alfieri, D.

Alfonso, P. V.

P. V. Alfonso, T. A. Irwing, T. Q. Carina, and C. Santiago-Tepantlan, “Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies Wavelet family,” Proc. SPIE 7443, 744327 (2009).

Bogoni, L.

L. Bogoni and M. Hansen, “Pattern-selective color image fusion,” Pattern Recognit. 34(8), 1515–1526 (2001).
[CrossRef]

Bouaoune, Y.

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

Bunel, P.

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

Burt, P. J.

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[CrossRef]

Carina, T. Q.

P. V. Alfonso, T. A. Irwing, T. Q. Carina, and C. Santiago-Tepantlan, “Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies Wavelet family,” Proc. SPIE 7443, 744327 (2009).

Cheng, H. D.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognit. 34(12), 2259–2281 (2001).
[CrossRef]

Coppola, G.

Corwin, A. D.

Dali, Z.

De Nicola, S.

Delechelle, E.

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

Dixon, E. L.

Equis, S.

Feng, H.

H. Zhao, Q. Li, and H. Feng, “Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map,” Image Vis. Comput. 26(9), 1285–1295 (2008).
[CrossRef]

Ferraro, P.

Filkins, R. J.

Finizio, A.

Grilli, S.

Guihong, Q.

Hansen, M.

L. Bogoni and M. Hansen, “Pattern-selective color image fusion,” Pattern Recognit. 34(8), 1515–1526 (2001).
[CrossRef]

Huang, N. E.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Irwing, T. A.

P. V. Alfonso, T. A. Irwing, T. Q. Carina, and C. Santiago-Tepantlan, “Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies Wavelet family,” Proc. SPIE 7443, 744327 (2009).

Jacquot, P.

Javidi, B.

Jeong, Y. J.

Jiang, X. H.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognit. 34(12), 2259–2281 (2001).
[CrossRef]

Kenny, K. B.

Kim, J. N.

Li, B.

B. Li and H. Lv, “Pixel level image fusion scheme based on accumulated gradient and PCA transform,” J. Commun. Comput. 6, 49–54 (2009).

Li, Q.

H. Zhao, Q. Li, and H. Feng, “Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map,” Image Vis. Comput. 26(9), 1285–1295 (2008).
[CrossRef]

Li, S.

S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput. 26(7), 971–979 (2008).
[CrossRef]

Liu, C.

J. Yang, C. Liu, and L. Zhang, “Color space normalization: Enhancing the discriminating power of color spaces for face recognition,” Pattern Recognit. 43(4), 1454–1466 (2010).
[CrossRef]

Z. Liu and C. Liu, “Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition,” Comput. Vis. Image Underst. 111(3), 249–262 (2008).
[CrossRef]

Liu, H. H.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Liu, Z.

Z. Liu and C. Liu, “Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition,” Comput. Vis. Image Underst. 111(3), 249–262 (2008).
[CrossRef]

Liu, Z. X.

Z. X. Liu and S. L. Peng, “Directional EMD and its application to texture segmentation,” Sci. China Ser. F, Inf. Sci. 48(3), 354–365 (2005).
[CrossRef]

Long, S. R.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Lv, H.

B. Li and H. Lv, “Pixel level image fusion scheme based on accumulated gradient and PCA transform,” J. Commun. Comput. 6, 49–54 (2009).

Niang, O.

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

Nunes, J. C.

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

Peng, S. L.

Z. X. Liu and S. L. Peng, “Directional EMD and its application to texture segmentation,” Sci. China Ser. F, Inf. Sci. 48(3), 354–365 (2005).
[CrossRef]

Pierattini, G.

Pingfan, Y.

Santiago-Tepantlan, C.

P. V. Alfonso, T. A. Irwing, T. Q. Carina, and C. Santiago-Tepantlan, “Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies Wavelet family,” Proc. SPIE 7443, 744327 (2009).

Shen, L.

Shen, Z.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Shih, H. H.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Striano, V.

Sun, Y.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognit. 34(12), 2259–2281 (2001).
[CrossRef]

Tasimi, K.

Tung, C. C.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Wang, J.

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognit. 34(12), 2259–2281 (2001).
[CrossRef]

Wu, M. C.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Yang, B.

S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput. 26(7), 971–979 (2008).
[CrossRef]

Yang, J.

J. Yang, C. Liu, and L. Zhang, “Color space normalization: Enhancing the discriminating power of color spaces for face recognition,” Pattern Recognit. 43(4), 1454–1466 (2010).
[CrossRef]

Yazdanfar, S.

Yen, N. C.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Yin, Q.

Zaveri, M.

T. Zaveri and M. Zaveri, “A Novel Two Step Region Based Multifocus Image Fusion Method,” Int. J. Comput. Electr. Eng. 2, 86–91 (2010).

Zaveri, T.

T. Zaveri and M. Zaveri, “A Novel Two Step Region Based Multifocus Image Fusion Method,” Int. J. Comput. Electr. Eng. 2, 86–91 (2010).

Zhang, L.

J. Yang, C. Liu, and L. Zhang, “Color space normalization: Enhancing the discriminating power of color spaces for face recognition,” Pattern Recognit. 43(4), 1454–1466 (2010).
[CrossRef]

Zhao, H.

H. Zhao, Q. Li, and H. Feng, “Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map,” Image Vis. Comput. 26(9), 1285–1295 (2008).
[CrossRef]

Zheng, Q.

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Comput. Vis. Image Underst. (1)

Z. Liu and C. Liu, “Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition,” Comput. Vis. Image Underst. 111(3), 249–262 (2008).
[CrossRef]

IEEE Trans. Commun. (1)

P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[CrossRef]

Image Vis. Comput. (3)

S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image Vis. Comput. 26(7), 971–979 (2008).
[CrossRef]

J. C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, and P. Bunel, “Image analysis by bidimensional empirical mode decomposition,” Image Vis. Comput. 21(12), 1019–1026 (2003).
[CrossRef]

H. Zhao, Q. Li, and H. Feng, “Multi-focus color image fusion in the HSI space using the sum-modified-laplacian and a coarse edge map,” Image Vis. Comput. 26(9), 1285–1295 (2008).
[CrossRef]

Int. J. Comput. Electr. Eng. (1)

T. Zaveri and M. Zaveri, “A Novel Two Step Region Based Multifocus Image Fusion Method,” Int. J. Comput. Electr. Eng. 2, 86–91 (2010).

J. Commun. Comput. (1)

B. Li and H. Lv, “Pixel level image fusion scheme based on accumulated gradient and PCA transform,” J. Commun. Comput. 6, 49–54 (2009).

Opt. Express (5)

Pattern Recognit. (3)

H. D. Cheng, X. H. Jiang, Y. Sun, and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognit. 34(12), 2259–2281 (2001).
[CrossRef]

J. Yang, C. Liu, and L. Zhang, “Color space normalization: Enhancing the discriminating power of color spaces for face recognition,” Pattern Recognit. 43(4), 1454–1466 (2010).
[CrossRef]

L. Bogoni and M. Hansen, “Pattern-selective color image fusion,” Pattern Recognit. 34(8), 1515–1526 (2001).
[CrossRef]

Proc. R. Soc. Lond. A (1)

N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proc. R. Soc. Lond. A 454(1971), 903–995 (1998).
[CrossRef]

Proc. SPIE (1)

P. V. Alfonso, T. A. Irwing, T. Q. Carina, and C. Santiago-Tepantlan, “Multifocus microscope color image fusion based on Daub(2) and Daub(4) kernels of the Daubechies Wavelet family,” Proc. SPIE 7443, 744327 (2009).

Sci. China Ser. F, Inf. Sci. (1)

Z. X. Liu and S. L. Peng, “Directional EMD and its application to texture segmentation,” Sci. China Ser. F, Inf. Sci. 48(3), 354–365 (2005).
[CrossRef]

Other (4)

L. Li, J. Le, and J. Yang, “Improved Method of Multi-focal Plane Micro-image Fusion,” in Proceedings of IEEE 9th International Conference on Electronic Measurement & Instruments (Institute of Electrical and Electronics Engineers, Beijing, China, 2009), pp. 4–417–4–421.

M. B. Bernini, A. Federico, and G. H. Kaufmann, “Denoising of digital speckle pattern interferometry fringes by means of Bidimensional Empirical Mode Decomposition,” Proc. SPIE 7063, 70630D–1–70630D −7 (2008).

T. Zaveri, M. Zaveri, V. Shah, and N. Patel, “A Novel Region Based Multifocus Image Fusion Method,” in Proceedings of IEEE International Conference on Digital Image Processing (Institute of Electrical and Electronics Engineers, Bangkok, Thailand, 2009), pp. 50–54.

W. Liu, J. Huang, and Y. Zhao, “Image Fusion Based on PCA and Undecimated Discrete Wavelet Transform,” in Proceedings of 13th International Conference on Neural Information Processing, ICONIP, I. King et al., eds. (Academic, Hong Kong, China, 2006), pp. 481–488.

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Figures (8)

Fig. 1
Fig. 1

Schematic flowchart of the proposed algorithm.

Fig. 2
Fig. 2

Example of color model transformation from RGB to YIQ. (a) Lena standard image in RGB color model. (b) Y component. (c) I component. (d) Q component.

Fig. 3
Fig. 3

BEMD process.

Fig. 4
Fig. 4

Example of the BEMD process. (a) Lena standard gray level image. (b) IMF(1). (c) IMF(2). (d) IMF(3). (e) Residue.

Fig. 5
Fig. 5

The first set of experimental images and fusion results. (a) Source image focused on left portion. (b) Source image focused on right portion. (c) Fused image using DWT. (d) Fused image using BEMD.

Fig. 6
Fig. 6

The second set of experimental images and fusion results. (a) Source image focused on the left two cells. (b) Source image focused on the middle cell. (c) Source image focused on the right two cells. (d) Fused image using DWT. (e) Fused image using BEMD.

Fig. 7
Fig. 7

The third set of experimental images and fusion results. (a) Source image focused on inner portion. (b) Source image focused on outer portion. (c) Fused image using DWT. (d) Fused image using BEMD.

Fig. 8
Fig. 8

Comparison of different color models. (a) Entropy. (b) Standard Difference (STD). (c) Average Gradient (AG). (d) Spatial Frequency (SF).

Tables (1)

Tables Icon

Table 1 Comparison of objective criteria of DWT and BEMD fusion algorithms

Equations (11)

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

[ Y I Q ] = [ 0.299 0.587 0.114 0.596 0.274 0.322 0.211 0.523 0.311 ] [ R G B ] .
[ R G B ] = [ 1.000 0.956 0.621 1.000 0.272 0.647 1.000 1.106 1.703 ] [ Y I Q ] .
S D = x = 0 X y = 0 Y [ I j 1 ( x , y ) I j ( x , y ) ] 2 I j 1 2 ( x , y ) .
S D = max | E m | max | Re s d u e | .
Im F = k = 1 n p k Im k .
I M F F j ( x , y ) = max n { a = l l b = l l | I M F i j ( x + a , y + b ) | } .
E n t r o p y = k ( i = 0 L 1 ( p k ) i log 2 ( p k ) i ) , k = R , G , B .
S T D = k ( i = 1 M j = 1 N ( Z k ( i , j ) μ k ) 2 / ( M × N ) ) , k = R , G , B .
A G = k i = 1 M 1 j = 1 N 1 ( ( Z k ( x i , y j ) x i ) 2 + ( Z k ( x i , y j ) y i ) 2 ) / 2 ( M 1 ) × ( N 1 ) , k = R , G , B .
S F = k ( R F k 2 + C F k 2 ) , k = R , G , B .
R F k = 1 M × N i = 1 M j = 2 N [ Z k ( x i , y j ) Z k ( x i , y j 1 ) ] 2 , C F k = 1 M × N i = 2 M j = 1 N [ Z k ( x i , y j ) Z k ( x i 1 , y j ) ] 2 .

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