ISAR Image Reconstruction with Heavily Corrupted Data Based on Normal Inverse Gaussian Model

  • Saeed Jafari
  • Farokh Hodjat KashaniEmail author
  • Ayaz Ghorbani
Research Article


Inverse synthetic aperture radar (ISAR) is a potent radar system which generates two-dimensional signal on the range-Doppler domain by using target’s motion. ISAR imaging of targets is an important tool for automatic target recognition and classification in the defense and aerospace industry. In this paper, we focus upon the problem of ISAR imaging at low signal to noise ratio (SNR). Nonsubsampled directional filter bank (NSDFB) is a very useful tool in studying the directional features in two-dimensional signals. This paper offers a novel ISAR imaging approach by using NSDFB coefficients modeling. Bayesian maximum a posteriori is used where normal inverse Gaussian model is presumed for estimating ISAR image at low SNR. We applied NSDFB transform to ISAR image and implemented procedure to describe the characteristics of the algorithm. Both simulated and real ISAR data have been tested. The proposed technique keeps a balance between feature preservation and noise suppression. Finally, experimental results show that the offered technique outperforms other in terms of visual assessment and image evaluation parameters.


Inverse synthetic aperture radar (ISAR) Normal inverse Gaussian (NIG) model Nonsubsampled directional filter bank (NSDFB) Bayesian maximum a posteriori 


  1. Achim, A., Kuruoglu, E. E., & Zerubia, J. (2006). SAR image filtering based on the heavy-tailed Rayleigh model. IEEE Transactions on Image Processing, 15(9), 2686–2693.Google Scholar
  2. Amirmazlaghani, M., & Amindavar, H. (2012). A novel sparse method for despeckling SAR images. IEEE Transactions on Geoscience and Remote Sensing, 50(12), 5024–5032.Google Scholar
  3. Bamberger, R. H. (1993). New results on two and three dimensional directional filter banks. In Conference on signals, systems and computers, 1993 (pp. 1286–1290). IEEE.Google Scholar
  4. Bamberger, R. H., & Smith, M. J. (1992). A filter bank for the directional decomposition of images: Theory and design. IEEE Transactions on Signal Processing, 40(4), 882–893.Google Scholar
  5. Bao, Z., Sun, C., & Xing, M. (2001). Time-frequency approaches to ISAR imaging of maneuvering targets and their limitations. IEEE Transactions on Aerospace and Electronic Systems, 37(3), 1091–1099.Google Scholar
  6. Buades, A., Coll, B., & Morel, J.-M. (2005a). A non-local algorithm for image denoising. In IEEE computer society conference on computer vision and pattern recognition CVPR 2005 (Vol. 2, pp. 60–65). IEEE.Google Scholar
  7. Buades, A., Coll, B., & Morel, J.-M. (2005b). A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation, 4(2), 490–530.Google Scholar
  8. Candes, E. J., & Donoho, D. L. (2000). Curvelets: A surprisingly effective nonadaptive representation for objects with edges. Stanford: Stanford Univ Ca Dept. of Statistics.Google Scholar
  9. Chang, S. G., Yu, B., & Vetterli, M. (2000). Spatially adaptive wavelet thresholding with context modeling for image denoising. IEEE Transactions on Image Processing, 9(9), 1522–1531.Google Scholar
  10. Chen, V. C., & Ling, H. (2002). Time-frequency transforms for radar imaging and signal analysis. London: Artech House.Google Scholar
  11. Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2011). Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3973–3985.Google Scholar
  12. Cooke, T., Martorella, M., Haywood, B., & Gibbins, D. (2006). Use of 3D ship scatterer models from ISAR image sequences for target recognition. Digital Signal Processing, 16(5), 523–532.Google Scholar
  13. Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia: Society for industrial and applied mathematics (SIAM).Google Scholar
  14. Do, M. N., & Vetterli, M. (2002). Contourlets: A directional multiresolution image representation. In International conference on image processing, 2002 (Vol. 1, pp. I–I). IEEE.Google Scholar
  15. Do, M. N., & Vetterli, M. (2005). The contourlet transform: An efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091–2106.Google Scholar
  16. Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81, 425–455.Google Scholar
  17. Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the american Statistical Association, 90(432), 1200–1224.Google Scholar
  18. Duan, H., Zhang, L., Fang, J., Huang, L., & Li, H. (2015). Pattern-coupled sparse Bayesian learning for inverse synthetic aperture radar imaging. IEEE Signal Processing Letters, 22(11), 1995–1999.Google Scholar
  19. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12), 3736–3745.Google Scholar
  20. Eldar, Y. C., & Kutyniok, G. (2012). Compressed sensing: Theory and applications. Cambridge: Cambridge University Press.Google Scholar
  21. Eslami, R., & Radha, H. (2007). A new family of nonredundant transforms using hybrid wavelets and directional filter banks. IEEE Transactions on Image Processing, 16(4), 1152–1167.Google Scholar
  22. Hanssen, A., & Øigård, T. (2001). The normal inverse Gaussian distribution as a flexible model for heavy tailed processes. In Proceedings of IEEE-EURASIP workshop on nonlinear signal and image processing, 3–6 June 2001, Baltimore, Maryland, USA.Google Scholar
  23. Hyvärinen, A. (1999). Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. Neural Computation, 11(7), 1739–1768.Google Scholar
  24. Jafari, S., & Ghofrani, S. (2016). Using two coefficients modeling of nonsubsampled shearlet transform for despeckling. Journal of Applied Remote Sensing, 10(1), 015002.Google Scholar
  25. Jafari, S., Kashani, F. H., & Ghorbani, A. (2018). ISAR image reconstruction with heavily corrupted data based on two-sided generalized gamma model. Manuscript submitted in Journal of ACES.Google Scholar
  26. Kingsbury, N. (1999). Image processing with complex wavelets. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 357(1760), 2543–2560.Google Scholar
  27. Labate, D., Lim, W.-Q., Kutyniok, G., & Weiss, G. (2005). Sparse multidimensional representation using shearlets. In Optics and photonics (pp. 59140U–59140U-59149). International Society for Optics and Photonics.Google Scholar
  28. Le Pennec, E., & Mallat, S. (2005). Sparse geometric image representations with bandelets. IEEE Transactions on Image Processing, 14(4), 423–438.Google Scholar
  29. Lenzen, F. (2006). Statistical regularization and Denoising. Austria: Leopold Franzens University Innsbruck.Google Scholar
  30. Li, H.-C., Hong, W., Wu, Y.-R., & Fan, P.-Z. (2013). Bayesian wavelet shrinkage with heterogeneity-adaptive threshold for SAR image despeckling based on generalized gamma distribution. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 2388–2402.Google Scholar
  31. Lu, Y. M., & Do, M. N. (2007). Multidimensional directional filter banks and surfacelets. IEEE Transactions on Image Processing, 16(4), 918–931.Google Scholar
  32. Mensa, D. L. (1991). High resolution radar cross-section imaging (Vol. 1, p. 280). Boston, MA: Artech House.Google Scholar
  33. Minh, N. (2002). Directional multiresolution image representations. Doctoral dissertation of Engineering, Computer Engineering, University of Canberra, Australia.Google Scholar
  34. Osher, S., Mao, Y., Dong, B., & Yin, W. (2011). Fast linearized Bregman iteration for compressive sensing and sparse denoising. Communications in Mathematical Sciences, 8, 93–111.Google Scholar
  35. Ozdemir, C. (2012). Inverse synthetic aperture radar imaging with MATLAB algorithms (Vol. 210). New York: Wiley.Google Scholar
  36. Park, J.-I., & Kim, K.-T. (2010). A comparative study on ISAR imaging algorithms for radar target identification. Progress in Electromagnetics Research, 108, 155–175.Google Scholar
  37. Park, S.-I., Smith, M. J., & Mersereau, R. M. (2004). Improved structures of maximally decimated directional filter banks for spatial image analysis. IEEE Transactions on Image Processing, 13(11), 1424–1431.Google Scholar
  38. Sadreazami, H., Ahmad, M. O., & Swamy, M. (2016). A study on image denoising in contourlet domain using the alpha-stable family of distributions. Signal Processing, 128, 459–473.Google Scholar
  39. She, Z., Gray, D. A., & Bogner, R. E. (2001). Autofocus for inverse synthetic aperture radar (ISAR) imaging. Signal Processing, 81(2), 275–291.Google Scholar
  40. Simoncelli, E. P., & Freeman, W. T. (1995). The steerable pyramid: A flexible architecture for multi-scale derivative computation. In International conference on image processing, 1995 (Vol. 3, pp. 444–447). IEEE.Google Scholar
  41. Stankovic, L. (2015). ISAR image analysis and recovery with unavailable or heavily corrupted data. IEEE Transactions on Aerospace and Electronic Systems, 51(3), 2093–2106.Google Scholar
  42. Steinberg, B. D., & Subbaram, H. M. (1991). Microwave imaging techniques. New York, NY: Wiley.Google Scholar
  43. Walker, J. L. (1980). Range-Doppler imaging of rotating objects. IEEE Transactions on Aerospace and Electronic Systems, 1(1), 23–52.Google Scholar
  44. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009). Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227.Google Scholar
  45. Yang, J., Wright, J., Huang, T. S., & Ma, Y. (2010). Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 19(11), 2861–2873.Google Scholar
  46. Zhang, D.-X., Gao, Q.-W., & Wu, X.-P. (2008a). Bayesian-based speckle suppression for SAR image using contourlet transform. Journal of Electronic Science and Technology of China, 6(1), 79–82.Google Scholar
  47. Zhang, X., & Jing, X. (2010). Image denoising in contourlet domain based on a normal inverse Gaussian prior. Digital Signal Processing, 20(5), 1439–1446.Google Scholar
  48. Zhang, Q., Yeo, T. S., Du, G., & Zhang, S. (2004). Estimation of three-dimensional motion parameters in interferometric ISAR imaging. IEEE Transactions on Geoscience and Remote Sensing, 42(2), 292–300.Google Scholar
  49. Zhang, Q., Yeo, T. S., Tan, H. S., & Luo, Y. (2008b). Imaging of a moving target with rotating parts based on the Hough transform. IEEE Transactions on Geoscience and Remote Sensing, 46(1), 291–299.Google Scholar
  50. Zhou, W., & Bovik, A. C. (2002). A universal image quality index. IEEE Signal Processing Letters, 9(3), 81–84. Scholar
  51. Zhou, W., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  • Saeed Jafari
    • 1
  • Farokh Hodjat Kashani
    • 1
    • 2
    Email author
  • Ayaz Ghorbani
    • 3
  1. 1.Electrical and Electronic Engineering DepartmentIslamic Azad University (IAU)TehranIran
  2. 2.Department of Electrical EngineeringIran University of Science and Technology (IUST)TehranIran
  3. 3.Department of Electrical EngineeringAmirkabir University of Technology (Tehran Polytechnic)TehranIran

Personalised recommendations