Abstract

In this paper, an infrared target adaptive detection method based on the receptive field and lateral inhibition (LI) of the human visual system is proposed. In the proposed method, the direction parameters of a Gabor filter are adaptively determined according to the gradient direction, so that edges in the image can be detected without manual intervention. Meanwhile, background prediction based on LI is used for regulating the gray value in the image to achieve background suppression and target enhancement. Experimental results indicate that the proposed method can extract both the small target and the area target from a complex background, and has satisfactory target detection ability.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

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    [Crossref]
  2. P. Wang, J. Tian, and C. Gao, “Infrared small target detection using directional highpass filters based on LS-SVM,” Electron. Lett. 45, 156–158 (2009).
    [Crossref]
  3. L. Yang, J. Yang, and K. Yang, “Adaptive detection for infrared small target under sea-sky complex background,” Electron. Lett. 40, 1083–1085 (2004).
    [Crossref]
  4. C. Yang, J. Ma, S. Qi, J. Tian, S. Zheng, and X. Tian, “Directional support value of Gaussian transformation for infrared small target detection,” Appl. Opt. 54, 2255–2265 (2015).
    [Crossref]
  5. W. Meng, T. Jin, and X. Zhao, “Adaptive method of dim small object detection with heavy clutter,” Appl. Opt. 52, D64–D74 (2013).
    [Crossref]
  6. X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
    [Crossref]
  7. X. Wang and Z. Tang, “Combining wavelet packets with higher-order statistics for infrared small targets detection,” Infrared Laser Eng. 38, 915–920 (2009).
  8. T. Soni, J. R. Zeidler, and W. Ku, “Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
    [Crossref]
  9. D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
    [Crossref]
  10. K. Xie, K. Fu, T. Zhou, J. Zhang, J. Yang, and Q. Wu, “Small target detection based on accumulated center-surround difference measure,” Infrared Phys. Technol. 67, 229–236 (2014).
    [Crossref]
  11. S. Kim and J. Lee, “Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track,” Pattern Recogn. 45, 393–406 (2012).
    [Crossref]
  12. X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
    [Crossref]
  13. X. Wang, G. Lv, and L. Xu, “Infrared dim target detection based on visual attention,” Infrared Phys. Technol. 55, 513–521 (2012).
    [Crossref]
  14. S. Qi, D. Ming, J. Ma, X. Sun, and J. Tian, “Robust method for infrared small-target detection based on Boolean map visual theory,” Appl. Opt. 53, 3929–3940 (2014).
    [Crossref]
  15. S. Kim, Y. Yang, J. Lee, and Y. Park, “Small target detection utilizing robust methods of the human visual system for IRST,” J. Infrared Millim. Terahertz Waves 30, 994–1011 (2009).
    [Crossref]
  16. J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
    [Crossref]
  17. C. Grigorescu, N. Petkov, and M. A. Westenberg, “Improved contour detection by non-classical receptive field inhibition,” in Biologically Motivated Computer Vision (Springer, 2002), Vol. 2525, pp. 50–59.
  18. C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour detection based on nonclassical receptive field inhibition,” IEEE Trans. Image Process. 12, 729–739 (2003).
    [Crossref]
  19. H. Duan, Y. Deng, X. Wang, and C. Xu, “Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention,” PLoS ONE 8, e72035 (2013).
    [Crossref]
  20. M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
    [Crossref]
  21. D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex,” J. Physiol. 160, 106–154 (1962).
    [Crossref]
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    [Crossref]
  23. Y. Wang, X. Qi, J. Xing, and D. Yu, “Extended Gabor function model and simulation of some characteristic curves of receptive field,” Sci. China 4, 386–393 (1989).
  24. H. K. Hartline, “The response of single optic nerve fibers of the vertebrate eye to illumination of the retina,” Am. J. Physiol. 121, 400–415 (1938).
  25. C. Zhang and H. Duan, “Biological lateral inhibition and Electimize approach to template matching,” Optik 126, 769–773 (2015).
    [Crossref]
  26. S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
    [Crossref]
  27. Z. Pian and X. Meng, “Contour detection model based on biological visual perception,” J. Infrared Millim. Waves 28, 362–366 (2009).
    [Crossref]
  28. J. Chen, Y. Ren, and G. Zheng, “Contour detection model based on mechanisms of visual perception in environment of low contrast,” Int. J. Pattern Recognit. Artif. Intell. 25, 845–850 (2012).
  29. H. Yun, Z. Wu, G. Wang, G. Tong, and H. Yang, “Image enhancement algorithm based on improved lateral inhibition network,” Infrared Phys. Technol. 76, 308–314 (2016).
    [Crossref]
  30. W. Zhang, M. Cong, and L. Wang, “Algorithms for optical weak small targets detection and tracking: review,” in IEEE International Conference on Neural Networks and Signal Processing (2004), Vol. 1, pp. 643–647.

2016 (2)

J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
[Crossref]

H. Yun, Z. Wu, G. Wang, G. Tong, and H. Yang, “Image enhancement algorithm based on improved lateral inhibition network,” Infrared Phys. Technol. 76, 308–314 (2016).
[Crossref]

2015 (3)

C. Zhang and H. Duan, “Biological lateral inhibition and Electimize approach to template matching,” Optik 126, 769–773 (2015).
[Crossref]

S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
[Crossref]

C. Yang, J. Ma, S. Qi, J. Tian, S. Zheng, and X. Tian, “Directional support value of Gaussian transformation for infrared small target detection,” Appl. Opt. 54, 2255–2265 (2015).
[Crossref]

2014 (3)

S. Qi, D. Ming, J. Ma, X. Sun, and J. Tian, “Robust method for infrared small-target detection based on Boolean map visual theory,” Appl. Opt. 53, 3929–3940 (2014).
[Crossref]

K. Xie, K. Fu, T. Zhou, J. Zhang, J. Yang, and Q. Wu, “Small target detection based on accumulated center-surround difference measure,” Infrared Phys. Technol. 67, 229–236 (2014).
[Crossref]

X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
[Crossref]

2013 (2)

H. Duan, Y. Deng, X. Wang, and C. Xu, “Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention,” PLoS ONE 8, e72035 (2013).
[Crossref]

W. Meng, T. Jin, and X. Zhao, “Adaptive method of dim small object detection with heavy clutter,” Appl. Opt. 52, D64–D74 (2013).
[Crossref]

2012 (3)

J. Chen, Y. Ren, and G. Zheng, “Contour detection model based on mechanisms of visual perception in environment of low contrast,” Int. J. Pattern Recognit. Artif. Intell. 25, 845–850 (2012).

X. Wang, G. Lv, and L. Xu, “Infrared dim target detection based on visual attention,” Infrared Phys. Technol. 55, 513–521 (2012).
[Crossref]

S. Kim and J. Lee, “Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track,” Pattern Recogn. 45, 393–406 (2012).
[Crossref]

2011 (2)

M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
[Crossref]

D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
[Crossref]

2010 (1)

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[Crossref]

2009 (4)

X. Wang and Z. Tang, “Combining wavelet packets with higher-order statistics for infrared small targets detection,” Infrared Laser Eng. 38, 915–920 (2009).

P. Wang, J. Tian, and C. Gao, “Infrared small target detection using directional highpass filters based on LS-SVM,” Electron. Lett. 45, 156–158 (2009).
[Crossref]

S. Kim, Y. Yang, J. Lee, and Y. Park, “Small target detection utilizing robust methods of the human visual system for IRST,” J. Infrared Millim. Terahertz Waves 30, 994–1011 (2009).
[Crossref]

Z. Pian and X. Meng, “Contour detection model based on biological visual perception,” J. Infrared Millim. Waves 28, 362–366 (2009).
[Crossref]

2004 (1)

L. Yang, J. Yang, and K. Yang, “Adaptive detection for infrared small target under sea-sky complex background,” Electron. Lett. 40, 1083–1085 (2004).
[Crossref]

2003 (1)

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour detection based on nonclassical receptive field inhibition,” IEEE Trans. Image Process. 12, 729–739 (2003).
[Crossref]

1999 (1)

S. D. Deshpande, M. H. Er, V. Ronda, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[Crossref]

1993 (1)

T. Soni, J. R. Zeidler, and W. Ku, “Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[Crossref]

1989 (1)

Y. Wang, X. Qi, J. Xing, and D. Yu, “Extended Gabor function model and simulation of some characteristic curves of receptive field,” Sci. China 4, 386–393 (1989).

1985 (1)

1962 (1)

D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex,” J. Physiol. 160, 106–154 (1962).
[Crossref]

1938 (1)

H. K. Hartline, “The response of single optic nerve fibers of the vertebrate eye to illumination of the retina,” Am. J. Physiol. 121, 400–415 (1938).

Bai, S.

X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
[Crossref]

Bai, X.

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[Crossref]

Bian, X.

D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
[Crossref]

Chan, P.

S. D. Deshpande, M. H. Er, V. Ronda, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[Crossref]

Chen, J.

J. Chen, Y. Ren, and G. Zheng, “Contour detection model based on mechanisms of visual perception in environment of low contrast,” Int. J. Pattern Recognit. Artif. Intell. 25, 845–850 (2012).

Cong, M.

W. Zhang, M. Cong, and L. Wang, “Algorithms for optical weak small targets detection and tracking: review,” in IEEE International Conference on Neural Networks and Signal Processing (2004), Vol. 1, pp. 643–647.

Dai, S.

S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
[Crossref]

Daugman, J. G.

Deng, Y.

H. Duan, Y. Deng, X. Wang, and C. Xu, “Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention,” PLoS ONE 8, e72035 (2013).
[Crossref]

Deshpande, S. D.

S. D. Deshpande, M. H. Er, V. Ronda, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[Crossref]

Dong, X.

X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
[Crossref]

Duan, H.

C. Zhang and H. Duan, “Biological lateral inhibition and Electimize approach to template matching,” Optik 126, 769–773 (2015).
[Crossref]

H. Duan, Y. Deng, X. Wang, and C. Xu, “Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention,” PLoS ONE 8, e72035 (2013).
[Crossref]

Er, M. H.

S. D. Deshpande, M. H. Er, V. Ronda, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[Crossref]

Fu, K.

K. Xie, K. Fu, T. Zhou, J. Zhang, J. Yang, and Q. Wu, “Small target detection based on accumulated center-surround difference measure,” Infrared Phys. Technol. 67, 229–236 (2014).
[Crossref]

Gao, C.

P. Wang, J. Tian, and C. Gao, “Infrared small target detection using directional highpass filters based on LS-SVM,” Electron. Lett. 45, 156–158 (2009).
[Crossref]

Grigorescu, C.

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour detection based on nonclassical receptive field inhibition,” IEEE Trans. Image Process. 12, 729–739 (2003).
[Crossref]

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Improved contour detection by non-classical receptive field inhibition,” in Biologically Motivated Computer Vision (Springer, 2002), Vol. 2525, pp. 50–59.

Han, J.

J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
[Crossref]

Hartline, H. K.

H. K. Hartline, “The response of single optic nerve fibers of the vertebrate eye to illumination of the retina,” Am. J. Physiol. 121, 400–415 (1938).

Huang, J.

J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
[Crossref]

Huang, X.

X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
[Crossref]

Hubel, D. H.

D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex,” J. Physiol. 160, 106–154 (1962).
[Crossref]

Jin, T.

Kim, S.

S. Kim and J. Lee, “Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track,” Pattern Recogn. 45, 393–406 (2012).
[Crossref]

S. Kim, Y. Yang, J. Lee, and Y. Park, “Small target detection utilizing robust methods of the human visual system for IRST,” J. Infrared Millim. Terahertz Waves 30, 994–1011 (2009).
[Crossref]

Ku, W.

T. Soni, J. R. Zeidler, and W. Ku, “Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[Crossref]

Lee, J.

S. Kim and J. Lee, “Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track,” Pattern Recogn. 45, 393–406 (2012).
[Crossref]

S. Kim, Y. Yang, J. Lee, and Y. Park, “Small target detection utilizing robust methods of the human visual system for IRST,” J. Infrared Millim. Terahertz Waves 30, 994–1011 (2009).
[Crossref]

Li, P.

S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
[Crossref]

Li, Q.

M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
[Crossref]

Lin, Q.

M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
[Crossref]

Liu, J.

S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
[Crossref]

Liu, Q.

S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
[Crossref]

Lv, G.

X. Wang, G. Lv, and L. Xu, “Infrared dim target detection based on visual attention,” Infrared Phys. Technol. 55, 513–521 (2012).
[Crossref]

Ma, J.

Ma, Y.

J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
[Crossref]

Mei, X.

J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
[Crossref]

Meng, W.

Meng, X.

Z. Pian and X. Meng, “Contour detection model based on biological visual perception,” J. Infrared Millim. Waves 28, 362–366 (2009).
[Crossref]

Ming, D.

Park, Y.

S. Kim, Y. Yang, J. Lee, and Y. Park, “Small target detection utilizing robust methods of the human visual system for IRST,” J. Infrared Millim. Terahertz Waves 30, 994–1011 (2009).
[Crossref]

Peng, Z.

M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
[Crossref]

Petkov, N.

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour detection based on nonclassical receptive field inhibition,” IEEE Trans. Image Process. 12, 729–739 (2003).
[Crossref]

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Improved contour detection by non-classical receptive field inhibition,” in Biologically Motivated Computer Vision (Springer, 2002), Vol. 2525, pp. 50–59.

Pian, Z.

Z. Pian and X. Meng, “Contour detection model based on biological visual perception,” J. Infrared Millim. Waves 28, 362–366 (2009).
[Crossref]

Qi, S.

Qi, X.

Y. Wang, X. Qi, J. Xing, and D. Yu, “Extended Gabor function model and simulation of some characteristic curves of receptive field,” Sci. China 4, 386–393 (1989).

Ren, Y.

J. Chen, Y. Ren, and G. Zheng, “Contour detection model based on mechanisms of visual perception in environment of low contrast,” Int. J. Pattern Recognit. Artif. Intell. 25, 845–850 (2012).

Ronda, V.

S. D. Deshpande, M. H. Er, V. Ronda, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[Crossref]

Shen, L.

X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
[Crossref]

Shi, M.

M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
[Crossref]

Shi, W.

D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
[Crossref]

Soni, T.

T. Soni, J. R. Zeidler, and W. Ku, “Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[Crossref]

Sun, X.

Tang, Z.

X. Wang and Z. Tang, “Combining wavelet packets with higher-order statistics for infrared small targets detection,” Infrared Laser Eng. 38, 915–920 (2009).

Tian, J.

Tian, X.

Tong, G.

H. Yun, Z. Wu, G. Wang, G. Tong, and H. Yang, “Image enhancement algorithm based on improved lateral inhibition network,” Infrared Phys. Technol. 76, 308–314 (2016).
[Crossref]

Wang, D.

D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
[Crossref]

Wang, G.

H. Yun, Z. Wu, G. Wang, G. Tong, and H. Yang, “Image enhancement algorithm based on improved lateral inhibition network,” Infrared Phys. Technol. 76, 308–314 (2016).
[Crossref]

Wang, L.

W. Zhang, M. Cong, and L. Wang, “Algorithms for optical weak small targets detection and tracking: review,” in IEEE International Conference on Neural Networks and Signal Processing (2004), Vol. 1, pp. 643–647.

Wang, P.

P. Wang, J. Tian, and C. Gao, “Infrared small target detection using directional highpass filters based on LS-SVM,” Electron. Lett. 45, 156–158 (2009).
[Crossref]

Wang, X.

H. Duan, Y. Deng, X. Wang, and C. Xu, “Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention,” PLoS ONE 8, e72035 (2013).
[Crossref]

X. Wang, G. Lv, and L. Xu, “Infrared dim target detection based on visual attention,” Infrared Phys. Technol. 55, 513–521 (2012).
[Crossref]

X. Wang and Z. Tang, “Combining wavelet packets with higher-order statistics for infrared small targets detection,” Infrared Laser Eng. 38, 915–920 (2009).

Wang, Y.

Y. Wang, X. Qi, J. Xing, and D. Yu, “Extended Gabor function model and simulation of some characteristic curves of receptive field,” Sci. China 4, 386–393 (1989).

Westenberg, M. A.

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Contour detection based on nonclassical receptive field inhibition,” IEEE Trans. Image Process. 12, 729–739 (2003).
[Crossref]

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Improved contour detection by non-classical receptive field inhibition,” in Biologically Motivated Computer Vision (Springer, 2002), Vol. 2525, pp. 50–59.

Wiesel, T. N.

D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction, and functional architecture in the cat’s visual cortex,” J. Physiol. 160, 106–154 (1962).
[Crossref]

Wu, Q.

K. Xie, K. Fu, T. Zhou, J. Zhang, J. Yang, and Q. Wu, “Small target detection based on accumulated center-surround difference measure,” Infrared Phys. Technol. 67, 229–236 (2014).
[Crossref]

Wu, Z.

H. Yun, Z. Wu, G. Wang, G. Tong, and H. Yang, “Image enhancement algorithm based on improved lateral inhibition network,” Infrared Phys. Technol. 76, 308–314 (2016).
[Crossref]

Xiang, H.

S. Dai, Q. Liu, P. Li, J. Liu, and H. Xiang, “Study on infrared image detail enhancement algorithm based on adaptive lateral inhibition network,” Infrared Phys. Technol. 68, 10–14 (2015).
[Crossref]

Xie, K.

K. Xie, K. Fu, T. Zhou, J. Zhang, J. Yang, and Q. Wu, “Small target detection based on accumulated center-surround difference measure,” Infrared Phys. Technol. 67, 229–236 (2014).
[Crossref]

Xing, J.

Y. Wang, X. Qi, J. Xing, and D. Yu, “Extended Gabor function model and simulation of some characteristic curves of receptive field,” Sci. China 4, 386–393 (1989).

Xu, C.

H. Duan, Y. Deng, X. Wang, and C. Xu, “Small and dim target detection via lateral inhibition filtering and artificial bee colony based selective visual attention,” PLoS ONE 8, e72035 (2013).
[Crossref]

Xu, L.

X. Wang, G. Lv, and L. Xu, “Infrared dim target detection based on visual attention,” Infrared Phys. Technol. 55, 513–521 (2012).
[Crossref]

Yan, L.

D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
[Crossref]

Yang, C.

Yang, H.

H. Yun, Z. Wu, G. Wang, G. Tong, and H. Yang, “Image enhancement algorithm based on improved lateral inhibition network,” Infrared Phys. Technol. 76, 308–314 (2016).
[Crossref]

Yang, J.

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[Crossref]

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[Crossref]

High Power Laser Part. Beams (1)

M. Shi, Z. Peng, Q. Zhang, Q. Li, and Q. Lin, “Dim infrared target detection based on adaptive lateral inhibition network,” High Power Laser Part. Beams 23, 906–910 (2011).
[Crossref]

IEEE Geosci. Remote Sens. Lett. (1)

J. Han, Y. Ma, J. Huang, X. Mei, and J. Ma, “An infrared small target detecting algorithm based on human visual system,” IEEE Geosci. Remote Sens. Lett. 13, 452–456 (2016).
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X. Wang and Z. Tang, “Combining wavelet packets with higher-order statistics for infrared small targets detection,” Infrared Laser Eng. 38, 915–920 (2009).

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X. Dong, X. Huang, Y. Zheng, L. Shen, and S. Bai, “Infrared dim and small target detecting and tracking method inspired by human visual system,” Infrared Phys. Technol. 62, 100–109 (2014).
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J. Chen, Y. Ren, and G. Zheng, “Contour detection model based on mechanisms of visual perception in environment of low contrast,” Int. J. Pattern Recognit. Artif. Intell. 25, 845–850 (2012).

J. Infrared Millim. Terahertz Waves (1)

S. Kim, Y. Yang, J. Lee, and Y. Park, “Small target detection utilizing robust methods of the human visual system for IRST,” J. Infrared Millim. Terahertz Waves 30, 994–1011 (2009).
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D. Wang, T. Zhang, L. Yan, W. Shi, and X. Bian, “Improved level-set framework-based algorithm for small infrared target detection,” Opt. Eng. 50, 283 (2011).
[Crossref]

Optik (1)

C. Zhang and H. Duan, “Biological lateral inhibition and Electimize approach to template matching,” Optik 126, 769–773 (2015).
[Crossref]

Pattern Recogn. (2)

X. Bai and F. Zhou, “Analysis of new top-hat transformation and the application for infrared dim small target detection,” Pattern Recogn. 43, 2145–2156 (2010).
[Crossref]

S. Kim and J. Lee, “Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track,” Pattern Recogn. 45, 393–406 (2012).
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Y. Wang, X. Qi, J. Xing, and D. Yu, “Extended Gabor function model and simulation of some characteristic curves of receptive field,” Sci. China 4, 386–393 (1989).

Other (2)

C. Grigorescu, N. Petkov, and M. A. Westenberg, “Improved contour detection by non-classical receptive field inhibition,” in Biologically Motivated Computer Vision (Springer, 2002), Vol. 2525, pp. 50–59.

W. Zhang, M. Cong, and L. Wang, “Algorithms for optical weak small targets detection and tracking: review,” in IEEE International Conference on Neural Networks and Signal Processing (2004), Vol. 1, pp. 643–647.

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Figures (10)

Fig. 1.
Fig. 1.

Schematic and response characteristic of the receptive fields of simple cells. (a) The different types of RF. (b) The response of the receptive fields to the stimulus of various directions.

Fig. 2.
Fig. 2.

Process of background prediction based on LI.

Fig. 3.
Fig. 3.

Process of the proposed method.

Fig. 4.
Fig. 4.

Relationship curve among background suppression factor, detection probability, and K.

Fig. 5.
Fig. 5.

Original images and small target detection results processed by the different methods.

Fig. 6.
Fig. 6.

SCRG and BSF obtained by different methods for each original image in Fig. 5.

Fig. 7.
Fig. 7.

ROC curves obtained by different methods for each original image in Fig. 5.

Fig. 8.
Fig. 8.

Original images and area target detection results processed by the different methods.

Fig. 9.
Fig. 9.

SCRG and BSF obtained by the different methods for each original image in Fig. 8.

Fig. 10.
Fig. 10.

ROC curves obtained by the different methods for each original image in Fig. 8.

Equations (12)

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{G(x,y)=exp[μ2+λν22σ2]cos[2πfμ+φ]μ=xcosθ+ysinθ,ν=xsinθ+ycosθ,
R(x,y)=G(x,y)*I(x,y)=x=0M1y=0N1G(xxτ,yyτ)I(xτ,yτ),
L(x,y)=I(x,y)pmqnhmn(p,q)I(mp,nq),
{hmn(p,q)=exp(dij,pqρ)ρ=1I(x,y),
R(x,y)=max{R(x,y)|θ=π16,2π16,,π},
fx(x,y)=[101202101],fy(x,y)=[121000121],
θ=arctan[fy(x,y)fx(x,y)].
{G(x,y)=exp[μ2+λν22σ2]cos[2πfμ+φ]μ=xcosθ+ysinθν=xsin+ycosθθ=arctan[fy(x,y)fx(x,y)].
E(x,y)=I(x,y)(p,q)SW(p,q)I(mp,nq),
fout(x,y)=(1+K·E(x,y))·fin(x,y),
R(x,y)=(1+K·E(x,y))·(G(x,y)*I(x,y))=(1+K·E(x,y))·x=0M1y=0N1G(xxτ,yyτ)I(xτ,yτ),
SCRG=(S/C)out(S/C)in,BSF=CinCout,

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