access icon free Reweighted infrared patch image model for small target detection based on non-convex ℒp-norm minimisation and TV regularisation

© The Institution of Engineering and Technology
Received 17/12/2019, Accepted 26/03/2020, Revised 24/02/2020, Published 02/04/2020

Infrared small target detection in a complex background has always been a challenging task in an infrared detection system. The existing methods based on the infrared patch image (IPI) model have achieved a good result but are sensitive to the complex background. So, to effectively detect the small target in complex background, model based on the reweighted IPI model along with total variance (TV) is proposed in this study. In this study firstly, the problem of using nuclear norm minimisation (NNM) in the existing IPI-based methods is discussed, and a solution is proposed by replacing the existing NNM with the ℒp-norm minimisation of singular values in the existing IPI methods. Secondly, a TV regularisation term is added to the background patch image to suppress the noise and preserve the strong edges in the background. The proposed method is solved by the alternating direction method of the multiplier. The robustness of the proposed method is validated by experimenting with the large dataset of real infrared images as well as the synthetic images. The proposed method not only has good background suppression ability, but also enhances and detect the target well in comparisons with the other baseline methods.

Inspec keywords: image denoising; object detection; minimisation; infrared imaging

Other keywords: infrared detection system; synthetic images; background patch image; complex background; nonconvex ℒp-norm minimisation; infrared patch image model; target detection; background suppression ability; nuclear norm minimisation; TV regularisation term; NNM; baseline methods; infrared images; reweighted IPI model

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

References

    1. 1)
      • 33. Chen, G., Zhang, J., Li, D., et al: ‘Robust Kronecker product video denoising based on fractional-order total variation model’, Signal Process., 2016, 119, pp. 1–20.
    2. 2)
      • 34. Zuo, W., Meng, D., Zhang, L., et al: ‘A generalized iterated shrinkage algorithm for non-convex sparse coding’. 2013 IEEE Int. Conf. on Computer Vision (ICCV), Sydney, NSW, Australia, 2013, pp. 217–224.
    3. 3)
      • 3. Reed, I.S., Gagliardi, R.M., Stotts, L.B.: ‘Optical moving target detection with 3-D matched filtering’, IEEE Trans. Aerosp. Electron. Syst., 1988, 24, (4), pp. 327–336.
    4. 4)
      • 37. Bai, X., Zhou, F.: ‘Analysis of new top-hat transformation and the application for infrared dim small target detection’, Pattern Recognit., 2010, 43, (6), pp. 2145–2156.
    5. 5)
      • 20. Zhang, Z., Li, F., Zhao, M., et al: ‘Joint low-rank and sparse principal feature coding for enhanced robust representation and visual classification’, IEEE Trans. Image Process., 2016, 25, (6), pp. 2429–2442.
    6. 6)
      • 28. Quach, K.G., Doung, C.N., Luu, K., et al: ‘Non-convex online robust PCA: enhance sparsity via ℓp-norm minimization’, Comput. Vis. Image Underst., 2017, 158, pp. 126–140.
    7. 7)
      • 13. Dabov, K., Foi, A., Katkovnik, V., et al: ‘Image denoising by sparse 3-D transform-domain collaborative filtering’, IEEE Trans. Image Process., 2007, 16, (8), pp. 2080–2095.
    8. 8)
      • 35. Li, C.: ‘An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing’, 2010.
    9. 9)
      • 18. Zhang, Z., Ren, J., Li, S., et al: ‘Robust subspace discovery by block-diagonal adaptive locality-constrained representation’. Proc. of the 27th ACM Int. Conf. on multimedia, France, 2019, pp. 1569–1577.
    10. 10)
      • 17. Li, M., He, Y.-J., Zhang, J.: ‘Small infrared target detection based on low-rank representation’. Int. Conf. on Image and Graphics, Tianjin, People's Republic of China, 2015.
    11. 11)
      • 5. Deshpande, S.D., Meng, H.E., Venkateswarlu, R., et al: ‘Max-mean and max-median filters for detection of small targets’. SPIE's Int. Symp. on Optical Science, Engineering, and Instrumentation, Denver, CO, USA, 1999, pp. 74–83.
    12. 12)
      • 22. Dai, Y., Wu, Y., Song, Y., et al: ‘Non-negative infrared patch-image model: robust target-background separation via partial sum minimization of singular values’, Infrared Phys. Technol., 2017, 81, pp. 182–194.
    13. 13)
      • 4. Bae, T.-W., Kim, Y.-C., Ahn, S.-H., et al: ‘A novel two-dimensional LMS (TDLMS) using sub-sampling mask and step-size index for small target detection’, IEICE Electron. Express, 2010, 7, (3), pp. 112–117.
    14. 14)
      • 12. Wang, C., Qin, S.: ‘Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis’, Infrared Phys. Technol., 2015, 69, pp. 123–135.
    15. 15)
      • 36. Lin, Z., Chen, M., Ma, Y.: ‘The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices’, arXiv preprint arXiv:1009.5055, 2010.
    16. 16)
      • 39. Gu, Y., Wang, C., Liu, B., et al: ‘A kernel-based nonparametric regression method for clutter removal in infrared small-target detection applications’, IEEE Geosci. Remote Sens. Lett., 2010, 7, (3), pp. 469–473.
    17. 17)
      • 30. Candes, E.J., Li, X., Ma, Y., et al: ‘Robust principal component analysis?’, JACM, 2011, 58, (3), p. 11.
    18. 18)
      • 7. Kim, S.: ‘Min-local-LoG filter for detecting small targets in cluttered background’, Electron. Lett., 2011, 47, (2), pp. 105–106.
    19. 19)
      • 6. Itti, L., Koch, C., Niebur, E.: ‘A model of saliency-based visual attention for rapid scene analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (11), pp. 1254–1259.
    20. 20)
      • 15. Gu, S., Xie, Q., Meng, D., et al: ‘Weighted nuclear norm minimization and its applications to low level vision’, Int. J. Comput. Vis., 2017, 121, (2), pp. 183–208.
    21. 21)
      • 27. Chartrand, R.: ‘Exact reconstruction of sparse signals via nonconvex minimization’, IEEE Signal Process. Lett., 2007, 14, (10), pp. 707–710.
    22. 22)
      • 23. Guo, J., Wu, Y., Dai, Y.: ‘Small target detection based on reweighted infrared patch-image model’, IET Image Process., 2017, 12, (1), pp. 70–79.
    23. 23)
      • 32. Wang, Z., Li, H., Ling, Q., et al: ‘Robust temporal-spatial decomposition and its applications in video processing’, IEEE Trans. Circuits Syst. Video Technol., 2013, 23, (3), pp. 387–400.
    24. 24)
      • 9. Hu, T., Zhao, J.-J., Cao, Y., et al: ‘Infrared small target detection based on saliency and principle component analysis’, J. Infrared Millim. Waves, 2010, 29, (4), pp. 303–306.
    25. 25)
      • 38. Hilliard, C.I.: ‘Selection of a clutter rejection algorithm for real-time target detection from an airborne platform’, Proc. SPIE, 2000, 4048, pp. 74–84.
    26. 26)
      • 14. Zhang, L., Dong, W., Zhang, D., et al: ‘Two-stage image denoising by principal component analysis with local pixel grouping’, Pattern Recognit., 2010, 43, (41), pp. 1531–1549.
    27. 27)
      • 1. Gao, C., Meng, D., Yang, Y., et al: ‘Infrared patch-image model for small target detection in a single image’, IEEE Trans. Image Process., 2013, 22, (12), pp. 4996–5009.
    28. 28)
      • 24. Dai, Y., Wu, Y.: ‘Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, 10, (8), pp. 3752–3767.
    29. 29)
      • 8. Qi, S., Ma, J., Tao, C., et al: ‘A robust directional saliency-based method for infrared small-target detection under various complex backgrounds’, IEEE Geosci. Remote Sens. Lett., 2013, 10, (3), pp. 495–499.
    30. 30)
      • 11. Wang, X., Shen, S., Ning, C., et al: ‘A sparse representation-based method for infrared dim target detection under sea--sky background’, Infrared Phys. Technol., 2015, 71, pp. 347–355.
    31. 31)
      • 19. Zhang, Z., Li, F., Zhao, M., et al: ‘Robust neighborhood preserving projection by nuclear/L2,1-norm regularization for image feature extraction’, IEEE Trans. Image Process., 2017, 26, (4), pp. 1607–1622.
    32. 32)
      • 29. Rudin, L.I., Osher, S., Fatemi, E.: ‘Nonlinear total variation based noise removal algorithms’, Phys. D, Nonlinear Phenom., 1992, 60, (1–4), pp. 259–268.
    33. 33)
      • 26. Wang, X., Zhenming, P., Dehui, K., et al: ‘Infrared dim target detection based on total variation regularization and principal component pursuit’, Image Vis. Comput., 2017, 63, pp. 1–9.
    34. 34)
      • 21. Dai, Y., Wu, Y., Song, Y.: ‘Infrared small target and background separation via column-wise weighted robust principal component analysis’, Infrared Phys. Technol., 2016, 77, pp. 421–430.
    35. 35)
      • 2. Chen, C.P., Li, H., Wei, Y., et al: ‘A local contrast method for small infrared target detection’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (1), pp. 574–581.
    36. 36)
      • 25. Zhang, L., Peng, L., Zhang, T., et al: ‘Infrared small target detection via non-convex rank approximation minimization joint l2, 1 norm’, Remote Sens., 2018, 10, (11), pp. 1–34.
    37. 37)
      • 31. Wright, J., Ganesh, A., Rao, S., et al: ‘Robust principal component analysis: exact recovery of corrupted low-rank matrices via convex optimization’, Adv. Neural. Inf. Process. Syst., 2009, pp. 2080–2088.
    38. 38)
      • 16. He, Y., Li, M., Zhang, J., et al: ‘Small infrared target detection based on low-rank and sparse representation’, Infrared Phys. Technol., 2015, 68, pp. 98–109.
    39. 39)
      • 10. Gao, C., Su, H., Li, L., et al: ‘Small infrared target detection based on kernel principal component analysis’. 2012 5th Int. Congress on Image and Signal Processing (CISP), Chongqing, People's Republic of China, 2012.
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