Abstract

Nowadays, convergence and ghosting artifacts are common problems in scene-based nonuniformity correction (NUC) algorithms. In this study, we introduce the idea of space frequency to the scene-based NUC. Then the convergence speed factor is presented, which can adaptively change the convergence speed by a change of the scene dynamic range. In fact, the convergence speed factor role is to decrease the statistical data standard deviation. The nonuniformity space relativity characteristic was summarized by plenty of experimental statistical data. The space relativity characteristic was used to correct the convergence speed factor, which can make it more stable. Finally, real and simulated infrared image sequences were applied to demonstrate the positive effect of our algorithm.

© 2010 Optical Society of America

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  1. D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Adaptive retina-like preprocessing for imaging detector arrays,” in Proceedings of the IEEE International Conference on Neural Networks (IEEE, 1993), pp. 1955–1960.
    [CrossRef]
  2. D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” Proc. SPIE 1308, 224–233 (1990).
    [CrossRef]
  3. J. G. Harris and Y.-M. Chiang, “Nonuniformity correction of infrared image sequences using the constant-statistics constraint,” IEEE Trans. Image Proc. 8, 1148–1151 (1999).
    [CrossRef]
  4. H.-L. Qin, S.-Q. Liu, H.-X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Infrared and Millimeter Waves (IEEE, 2007), pp. 508–509.
  5. Y. Jian, S. Ruan, H. Zhou, and C. Liu, “An improved nonuniformity correction algorithm for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Intelligent Control and Automation (IEEE, 2006), pp. 10328–10331.
  6. M. Shehadeh and O. Kuybeda, “Robust nonuniformity correction in infrared images,” in Proceedings of the IEEE International Conference on Electrical and Electronics Engineers (IEEE, 2008), pp. 275–279.
  7. W. Qian, Q. Chen, and G. Gu, “Space low-pass and temporal high-pass nonuniformity correction algorithm,” Opt. Rev. 17, 24–29 (2010).
    [CrossRef]
  8. M. Schulz and L. Caldwell, “Nonuniformity correction and correctability of infrared focal plane arrays,” Infrared Phys. Technol. 36, 763–777 (1995).
    [CrossRef]

2010

W. Qian, Q. Chen, and G. Gu, “Space low-pass and temporal high-pass nonuniformity correction algorithm,” Opt. Rev. 17, 24–29 (2010).
[CrossRef]

1999

J. G. Harris and Y.-M. Chiang, “Nonuniformity correction of infrared image sequences using the constant-statistics constraint,” IEEE Trans. Image Proc. 8, 1148–1151 (1999).
[CrossRef]

1995

M. Schulz and L. Caldwell, “Nonuniformity correction and correctability of infrared focal plane arrays,” Infrared Phys. Technol. 36, 763–777 (1995).
[CrossRef]

1990

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” Proc. SPIE 1308, 224–233 (1990).
[CrossRef]

Caldwell, L.

M. Schulz and L. Caldwell, “Nonuniformity correction and correctability of infrared focal plane arrays,” Infrared Phys. Technol. 36, 763–777 (1995).
[CrossRef]

Caulfield, J. T.

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” Proc. SPIE 1308, 224–233 (1990).
[CrossRef]

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Adaptive retina-like preprocessing for imaging detector arrays,” in Proceedings of the IEEE International Conference on Neural Networks (IEEE, 1993), pp. 1955–1960.
[CrossRef]

Chen, Q.

W. Qian, Q. Chen, and G. Gu, “Space low-pass and temporal high-pass nonuniformity correction algorithm,” Opt. Rev. 17, 24–29 (2010).
[CrossRef]

Chiang, Y.-M.

J. G. Harris and Y.-M. Chiang, “Nonuniformity correction of infrared image sequences using the constant-statistics constraint,” IEEE Trans. Image Proc. 8, 1148–1151 (1999).
[CrossRef]

Gu, G.

W. Qian, Q. Chen, and G. Gu, “Space low-pass and temporal high-pass nonuniformity correction algorithm,” Opt. Rev. 17, 24–29 (2010).
[CrossRef]

Harris, J. G.

J. G. Harris and Y.-M. Chiang, “Nonuniformity correction of infrared image sequences using the constant-statistics constraint,” IEEE Trans. Image Proc. 8, 1148–1151 (1999).
[CrossRef]

Jian, Y.

Y. Jian, S. Ruan, H. Zhou, and C. Liu, “An improved nonuniformity correction algorithm for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Intelligent Control and Automation (IEEE, 2006), pp. 10328–10331.

Kuybeda, O.

M. Shehadeh and O. Kuybeda, “Robust nonuniformity correction in infrared images,” in Proceedings of the IEEE International Conference on Electrical and Electronics Engineers (IEEE, 2008), pp. 275–279.

Lai, R.

H.-L. Qin, S.-Q. Liu, H.-X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Infrared and Millimeter Waves (IEEE, 2007), pp. 508–509.

Liu, C.

Y. Jian, S. Ruan, H. Zhou, and C. Liu, “An improved nonuniformity correction algorithm for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Intelligent Control and Automation (IEEE, 2006), pp. 10328–10331.

Liu, S.-Q.

H.-L. Qin, S.-Q. Liu, H.-X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Infrared and Millimeter Waves (IEEE, 2007), pp. 508–509.

Qian, W.

W. Qian, Q. Chen, and G. Gu, “Space low-pass and temporal high-pass nonuniformity correction algorithm,” Opt. Rev. 17, 24–29 (2010).
[CrossRef]

Qin, H.-L.

H.-L. Qin, S.-Q. Liu, H.-X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Infrared and Millimeter Waves (IEEE, 2007), pp. 508–509.

Ruan, S.

Y. Jian, S. Ruan, H. Zhou, and C. Liu, “An improved nonuniformity correction algorithm for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Intelligent Control and Automation (IEEE, 2006), pp. 10328–10331.

Sarkady, K. A.

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” Proc. SPIE 1308, 224–233 (1990).
[CrossRef]

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Adaptive retina-like preprocessing for imaging detector arrays,” in Proceedings of the IEEE International Conference on Neural Networks (IEEE, 1993), pp. 1955–1960.
[CrossRef]

Schulz, M.

M. Schulz and L. Caldwell, “Nonuniformity correction and correctability of infrared focal plane arrays,” Infrared Phys. Technol. 36, 763–777 (1995).
[CrossRef]

Scribner, D. A.

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” Proc. SPIE 1308, 224–233 (1990).
[CrossRef]

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Adaptive retina-like preprocessing for imaging detector arrays,” in Proceedings of the IEEE International Conference on Neural Networks (IEEE, 1993), pp. 1955–1960.
[CrossRef]

Shehadeh, M.

M. Shehadeh and O. Kuybeda, “Robust nonuniformity correction in infrared images,” in Proceedings of the IEEE International Conference on Electrical and Electronics Engineers (IEEE, 2008), pp. 275–279.

Zhou, H.

Y. Jian, S. Ruan, H. Zhou, and C. Liu, “An improved nonuniformity correction algorithm for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Intelligent Control and Automation (IEEE, 2006), pp. 10328–10331.

Zhou, H.-X.

H.-L. Qin, S.-Q. Liu, H.-X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Infrared and Millimeter Waves (IEEE, 2007), pp. 508–509.

IEEE Trans. Image Proc.

J. G. Harris and Y.-M. Chiang, “Nonuniformity correction of infrared image sequences using the constant-statistics constraint,” IEEE Trans. Image Proc. 8, 1148–1151 (1999).
[CrossRef]

Infrared Phys. Technol.

M. Schulz and L. Caldwell, “Nonuniformity correction and correctability of infrared focal plane arrays,” Infrared Phys. Technol. 36, 763–777 (1995).
[CrossRef]

Opt. Rev.

W. Qian, Q. Chen, and G. Gu, “Space low-pass and temporal high-pass nonuniformity correction algorithm,” Opt. Rev. 17, 24–29 (2010).
[CrossRef]

Proc. SPIE

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Nonuniformity correction for staring IR focal plane arrays using scene-based techniques,” Proc. SPIE 1308, 224–233 (1990).
[CrossRef]

Other

D. A. Scribner, K. A. Sarkady, and J. T. Caulfield, “Adaptive retina-like preprocessing for imaging detector arrays,” in Proceedings of the IEEE International Conference on Neural Networks (IEEE, 1993), pp. 1955–1960.
[CrossRef]

H.-L. Qin, S.-Q. Liu, H.-X. Zhou, and R. Lai, “Nonuniformity correction algorithm based on wavelet transform for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Infrared and Millimeter Waves (IEEE, 2007), pp. 508–509.

Y. Jian, S. Ruan, H. Zhou, and C. Liu, “An improved nonuniformity correction algorithm for infrared focal plane arrays,” in Proceedings of the IEEE International Conference on Intelligent Control and Automation (IEEE, 2006), pp. 10328–10331.

M. Shehadeh and O. Kuybeda, “Robust nonuniformity correction in infrared images,” in Proceedings of the IEEE International Conference on Electrical and Electronics Engineers (IEEE, 2008), pp. 275–279.

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Figures (12)

Fig. 1
Fig. 1

J (a) Blackbody infrared image (temperature 40 ° C ) and (b) curve of J through L.

Fig. 2
Fig. 2

High-space-frequency part of the nonuniformity images: (a) sky, (b) 40 ° C blackbody, (c) 20 ° C blackbody, and (d) 70 ° C blackbody.

Fig. 3
Fig. 3

Distribution histogram of 10,000 high-space-frequency images at different pixels: (a) (102,184), (b) (209,196), (c) (54,81), and (d) (62,192).

Fig. 4
Fig. 4

Nonuniformity high-space-frequency part standard deviation of the whole image.

Fig. 5
Fig. 5

Three algorithms compared at the tenth frame (sky scene): (a) original image, (b) temporal high-pass algorithm, (c) SLTH algorithm, and (d) CSF algorithm.

Fig. 6
Fig. 6

Three algorithms compared at the sky and ground scene: (a) original image, (b) temporal high-pass algorithm, (c) SLTH algorithm, and (d) CSF algorithm.

Fig. 7
Fig. 7

Convergence speed factor of Fig. 6.

Fig. 8
Fig. 8

Virtual nonuniformity image sequences: (a) original uniform image and (b) image added with nonuniformity.

Fig. 9
Fig. 9

Curve of J for the virtual nonuniformity through L.

Fig. 10
Fig. 10

Comparison of the MSE of the three algorithms on S 2 .

Fig. 11
Fig. 11

Real image with virtual nonuniformity: (a) original image without nonuniformity and (b) image added with nonuniformity.

Fig. 12
Fig. 12

Comparison of the MSE of the three algorithms on S 3 .

Equations (23)

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y high ( i , j , k ) = y ( i , j , k ) y low ( i , j , k ) = y ( i , j , k ) y ( i , j , k ) H low ,
E [ y high ( i , j , k ) ] = l = k n + 1 k y high ( i , j , l ) n = n 1 n E [ y high ( i , j , k 1 ) ] + 1 n y high ( i , j , k ) ,
x ( i , j , k ) = y ( i , j , k ) E [ y high ( i , j , k ) ] .
H low ( L ) = [ 1 / L 1 / L 1 / L 1 / L 1 / L ] ,
y low = y H low ( L ) ,
H high ( L ) = [ 0 0 1 0 0 ] H low ( L ) = [ 1 / L 1 / L 1 1 / L 1 / L 1 / L ] .
y high = y H high ( L ) ,
y = y low + y high .
IP = i = 1 L j = 1 L H high ( i , j ) · H low ( i , j ) .
J = i j [ y low ( i , j ) y ¯ low ] 2 / M / N [ σ noise low ] 2 .
E [ y high ( i , j , k ) ] = [ 1 α ( i , j , k ) ] · E [ y high ( i , j , k 1 ) ] + α ( i , j , k ) · y high ( i , j , k ) .
p ( i , j , k ) = 1 2 π σ high ( i , j ) exp [ ( y high ( i , j , k ) m high ( i , j ) ) 2 σ high ( i , j ) 2 ] .
p ( i , j , k ) = 1 2 π σ high ( i , j ) exp [ ( y high ( i , j , k ) E [ y high ( i , j , k ) ] ) 2 σ high ( i , j ) 2 ] .
α ( i , j , k ) = α 0 · exp [ ( y high ( i , j , k ) E [ y high ( i , j , k ) ] ) 2 σ high ( i , j ) 2 ] .
α ( i , j , k ) = α 0 · exp [ ( y high ( i , j , k ) E [ y high ( i , j , k ) ] ) 2 ( σ high ) 2 ] .
α ( i , j , k ) = α 0 · exp [ ( y high ( i , j , k ) E [ y high ( i , j , k ) ] ) 2 ( σ high ) 2 ] · exp ( ( y high ( i , j , k ) m NU ( i , j ) ) 2 σ NU ( i , j ) 2 ) .
exp ( ( y high ( i , j , k ) m NU ( i , j ) ) 2 σ NU ( i , j ) 2 ) ,
exp ( ( y high ( i , j , k ) m NU ( i , j ) ) 2 σ NU ( i , j ) 2 )
y high ( i , j , k ) = y ( i , j , k ) y low ( i , j , k ) = y ( i , j , k ) y ( i , j , k ) H low ( L ) ,
α ( i , j , k ) = α 0 · exp [ ( y high ( i , j , k ) E [ y high ( i , j , k 1 ) ] ) 2 ( σ high ) 2 ] · exp ( ( y high ( i , j , k ) m NU ( i , j ) ) 2 σ NU ( i , j ) 2 ) ,
E [ y high ( i , j , k ) ] = [ 1 α ( i , j , k ) ] · E [ y high ( i , j , k 1 ) ] + α ( i , j , k ) · y high ( i , j , k ) ,
x ( i , j , k ) = y ( i , j , k ) E [ y high ( i , j , k ) ] .
MSE = i = 1 N j = 1 M ( I P ) 2 M × N .

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