access icon free Background suppression of small target image based on fast local reverse entropy operator

Background suppression is vitally important for the small target detection, which aims to enhance targets and improve the signal-to-noise ratio of small target images. Consequently, the study proposes a background suppression approach based on the fast local reverse entropy operator, which is designed according to the fact that the appearance of a small target could result in the great change of the value of local reverse entropy in the local region. The operator is adopted to suppress complex backgrounds of small target images in order to enhance small targets, and then bring about high probabilities of detection and low probabilities of false alarm in the small target detection. Both quantitative and qualitative analyses contribute to confirm the validity and efficiency of the proposed approach.

Inspec keywords: entropy; object detection; signal processing

Other keywords: target image; complex backgrounds; quantitative analysis; false alarm; background suppression; target detection; signal-to-noise ratio; local reverse entropy operator; qualitative analysis

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing

References

    1. 1)
      • 15. Qu, X.J., Chen, H., Peng, G.H.: ‘Novel detection method for infrared small targets using weighted information entropy’, J. Syst. Eng. Electron., 2012, 23, (6), pp. 838842.
    2. 2)
      • 3. Gao, C.Q.: ‘Small infrared target detection using sparse ring representation’, IEEE Trans. Aerosp. Electron. Syst., 2012, 27, (3), pp. 2130 (doi: 10.1109/MAES.2012.6196254).
    3. 3)
      • 13. Li, H., Xu, S.H., Li, L.Q.: ‘Dim target detection and tracking based on empirical mode decomposition’, Signal Process., Image Commun., 2008, 23, (10), pp. 788797 (doi: 10.1016/j.image.2008.10.001).
    4. 4)
      • 16. Deng, H., Liu, J.G., Li, H.: ‘EMD based infrared image target detection method,J. Infrared Millim. Terahertz Waves, 2009, 30, (11), pp. 12051215 (doi: 10.1007/s10762-009-9548-9).
    5. 5)
      • 1. Kim, S.H., Lee, J.Y.: ‘Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track’, Pattern Recognit., 2012, 45, (1), pp. 393406 (doi: 10.1016/j.patcog.2011.06.009).
    6. 6)
      • 18. Victor, J.D., Conte, M.M.: ‘Local image statistics: maximum-entropy constructions and perceptual salience’, J. Opt. Soc. Am. A Opt. Image Sci. Vis., 2012, 29, (7), pp. 13131345 (doi: 10.1364/JOSAA.29.001313).
    7. 7)
      • 6. Bae, T.W., Zhang, F., Kweon, I.S.: ‘Edge directional 2D LMS filter for infrared small target detection’, Infrared Phys. Technol., 2012, 55, (1), pp. 137145 (doi: 10.1016/j.infrared.2011.10.006).
    8. 8)
      • 12. Huang, K., Mao, X.: ‘Detectability of infrared small targets’, Phys. Technol., 2010, 53, (3), pp. 208217.
    9. 9)
      • 17. Pun, T.: ‘A new method for gray-level picture thresholding using the entropy of the histogram’, Comput. Inf. Sci., 1980, 2, (3), pp. 223237.
    10. 10)
      • 9. Wang, Q., Liu, G., Shi, Y.W.: ‘Detecting of multi-dim-small-target in sea or sky background based on higher-order cumulants and wavelet’, Recent Adv. Comput. Sci. Inf. Eng., 2012, 128, (1), pp. 497504 (doi: 10.1007/978-3-642-25792-6_75).
    11. 11)
      • 8. Cassent, D.P., Smokelin, J.S., Ye, A.: ‘Wavelet and gabor transforms for detection and recovery’, Opt. Eng., 1992, 31, (9), pp. 18931898 (doi: 10.1117/12.59913).
    12. 12)
      • 2. Bai, X.Z., Zhou, F.G.: ‘Analysis of new top-hat transformation and the application for infrared dim small target detection’, Pattern Recognit., 2010, 43, (6), pp. 21452156 (doi: 10.1016/j.patcog.2009.12.023).
    13. 13)
      • 7. Yang, L., Yang, J., Yang, K.: ‘Adaptive detection for infrared small target under sea-sky complex background’, Electron. Lett., 2004, 40, (17), pp. 10831085 (doi: 10.1049/el:20045204).
    14. 14)
      • 19. Śmieja, M., Tabor, J.: ‘Entropy of the mixture of sources and entropy dimension’, IEEE Trans. Inf. Theory, 2012, 58, (5), pp. 27192728 (doi: 10.1109/TIT.2011.2181820).
    15. 15)
      • 10. Deng, H., Liu, J.G., Chen, Z.: ‘Infrared small target detection based on modified local entropy and EMD’, Chin. Opt. Lett., 2010, 8, (1), pp. 2428 (doi: 10.3788/COL20100801.0024).
    16. 16)
      • 5. Pohlig, S.C.: ‘Spatial-temporal detection of electro-optic moving targets’, IEEE Trans. Aerosp. Electron. Syst., 1995, 31, (2), pp. 608616 (doi: 10.1109/7.381909).
    17. 17)
      • 4. Weng, J.Y., Cohen, P., Herniou, M.: ‘Camera calibration with distortion models and accuracy evaluation’, IEEE Trans. Pattern Anal. Mach. Intell., 1992, 14, (10), pp. 965980 (doi: 10.1109/34.159901).
    18. 18)
      • 14. Huang, S., Li, C., Liu, Y.: ‘Complex-values filtering based on the minimization of complex-error entropy’, IEEE Trans. Neural Netw. Learn. Syst., 2013, 24, (5), pp. 695708 (doi: 10.1109/TNNLS.2013.2241788).
    19. 19)
      • 20. Chen, B.D., Zhu, P.P., Principe, J.C.: ‘Survival information potential: a new criterion for adaptive system training’, IEEE Trans. Signal Process., 2012, 60, (3), pp. 11841194 (doi: 10.1109/TSP.2011.2178406).
    20. 20)
      • 11. Deng, H., Liu, J.G.: ‘Infrared small target detection based on the self-information map’, Infrared Phys. Technol., 2011, 54, (2), pp. 100107 (doi: 10.1016/j.infrared.2011.01.003).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2012.0240
Loading

Related content

content/journals/10.1049/iet-cvi.2012.0240
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading