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SAR Image Despeckling Via Structural Sparse Representation

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Abstract

A novel synthetic aperture radar (SAR) image despeckling method based on structural sparse representation is introduced. The proposed method utilizes the fact that different regions in SAR images correspond to varying terrain reflectivity. Therefore, SAR images can be split into a heterogeneous class (with a varied terrain reflectivity) and a homogeneous class (with a constant terrain reflectivity). In the proposed method, different sparse representation based despeckling schemes are designed by combining the different region characteristics in SAR images. For heterogeneous regions with rich structure and texture information, structural dictionaries are learned to appropriately represent varied structural characteristics. Specifically, each patch in these regions is sparsely coded with the best fitted structural dictionary, thus good structure preservation can be obtained. For homogenous regions without rich structure and texture information, the highly redundant photometric self-similarity is exploited to suppress speckle noise without introducing artifacts. That is achieved by firstly learning the sub-dictionary, then simultaneously sparsely coding for each group of photometrically similar image patches. Visual and objective experimental results demonstrate the superiority of the proposed method over the-state-of-the-art methods.

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References

  1. Goodman, J. (1976). Some fundamental properties of speckle. Journal of the Optical Society of America, 66(11), 1145–1150.

    Article  Google Scholar 

  2. Oliver, C., & Quegan, S. (2004). Understanding synthetic aperture radar images. Raleigh, NC: SciTech.

    Google Scholar 

  3. Lee, J. S., Jurkevich, I., Dewaele, P., Wambacq, P., & Oosterlinck, A. (1994). Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, 8(4), 313–340.

    Article  Google Scholar 

  4. Lee, J. S. (1980). Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(2), 165–168.

    Article  Google Scholar 

  5. Frost, V. S., Stiles, J. A., Shanmugan, K. S., & Holtzman, J. C. (1982). A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4(2), 157–166.

    Article  Google Scholar 

  6. Kuan, D. T., Sawchuk, A. A., Strand, T. C., & Chavel, P. (1985). Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2), 165–177.

    Article  Google Scholar 

  7. Lopes, A., Nezry, E., Touzi, R., & Laur, H. (1990). Maximum a posteriori speckle filtering and first order texture models in SAR images. In Proceedings of IGARSS (pp. 2409–2412). Washington, DC.

  8. Ranjani, J. J., & Thiruvengadan, S. (2010). Dual-tree complex wavelet transform based SAR despeckling using interscale dependence. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2723–2731.

    Article  Google Scholar 

  9. Amirmazlaghani, M., & Amindavar, H. (2012). A novel sparse method for despeckling SAR images. IEEE Transactions on Geoscience and Remote Sensing, 50(12), 5024–5032.

    Article  Google Scholar 

  10. Argenti, F., Bianchi, T., Lapini, A., & Alparone, L. (2012). Fast MAP despeckling based on Laplacian–Gaussian modeling of wavelet coefficients. IEEE Geoscience and Remote Sensing Letters, 9(1), 13–17.

    Article  Google Scholar 

  11. Deledalle, C., Denis, L., & Tupin, F. (2009). Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Transactions on Image Processing, 18(12), 2661–2672.

    Article  MathSciNet  Google Scholar 

  12. Buades, A., Coll, B., & Morel, J. M. (2005). A non-local algorithm for image denoising, In Proceedings of ICCV (pp. 60–65). Beijing.

  13. Parrilli, S., Poderico, M., Angelino, C. V., & Verdoliva, L. (2012). A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Transactions on Geoscience and Remote Sensing, 50(2), 606–616.

    Article  Google Scholar 

  14. Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080–2095.

    Article  MathSciNet  Google Scholar 

  15. Solbo, S., & Eltoft, T. (2004). Homomorphic wavelet-based statistical despeckling of SAR images. IEEE Transactions on Geoscience and Remote Sensing, 42(4), 711–721.

    Article  Google Scholar 

  16. Li, Y., Gong, H. L., Feng, D. F., & Zhang, Y. N. (2011). An adaptive method of speckle reduction and feature enhancement for SAR images based on curvelet transform and particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing, 49(8), 3105–3116.

    Article  Google Scholar 

  17. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12), 3736–3745.

    Article  MathSciNet  Google Scholar 

  18. Chatterjee, P., & Milanfar, P. (2009). Clustering-based denoising with locally learned dictionaries. IEEE Transactions on Image Processing, 18(7), 1438–1451.

    Article  MathSciNet  Google Scholar 

  19. Dong, W., Li, X., Zhang, L., & Shi, G. (2011). Sparsity-based image denoising via dictionary learning and structural clustering, In Proceedings of CVPR (pp. 457–464). Colorado Springs, CO.

  20. Dong, W., Zhang, L., Shi, G., & Wu, X. (2011). Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 20(7), 1838–1857.

    Article  MathSciNet  Google Scholar 

  21. Huang, Y., Moisan, L., Ng, M. K., & Zeng, T. (2012). Multiplicative noise removal via a learned dictionary. IEEE Transactions on Image Processing, 21(11), 4534–4543.

    Article  MathSciNet  Google Scholar 

  22. Hao, Y., Feng, X., & Xu, J. (2012). Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation. Signal Processing, 92(6), 1536–1549.

    Article  Google Scholar 

  23. Park, J. M., Song, W. J., & Pearlman, W. A. (1999). Speckle filtering of SAR images based on adaptive windowing. IEE Proceedings Vision, Image and Signal Processing, 146(4), 191–197.

    Article  Google Scholar 

  24. Bianchi, T., Argenti, F., & Alparone, L. (2008). Segmentation-based MAP despeckling of SAR images in the undecimated wavelet domain. IEEE Transactions on Geoscience and Remote Sensing, 46(9), 2728–2742.

    Article  Google Scholar 

  25. Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2009). Non-local sparse models for image restoration, In Proceedings of ICCV (pp. 2272–2279). Tokyo.

  26. Xie, H., Pierce, L. E., & Ulaby, F. T. (2002). Statistical properties of logarithmically transformed speckle. IEEE Transactions on Geoscience and Remote Sensing, 40(3), 721–727.

    Article  Google Scholar 

  27. Arsenault, H. H., & April, G. (1976). Properties of speckle integrated with a finite aperture and logarithmically transformed. Journal of the Optical Society of America, 66(11), 1160–1163.

    Article  Google Scholar 

  28. Xie, H., Pierce, L. E., & Ulaby, F. T. (2002). SAR speckle reduction using wavelet denoising and markov random field modeling. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2196–2212.

    Article  Google Scholar 

  29. Daubechies, I., DeVore, R., Fornasier, M., & Gunturk, C. S. (2010). Iteratively reweighted least squares minimization for sparse recovery. Communications on Pure and Applied Mathematics, 63(1), 1–38.

    Article  MathSciNet  MATH  Google Scholar 

  30. Zibulevsky, M., & Elad, M. (2010). L1–L2 optimization in signal and image processing. IEEE Signal Processing, 27(3), 76–88.

    Article  MathSciNet  Google Scholar 

  31. Lopés, A., Nezry, E., Touzi, R., & Laur, H. (1993). Structure detection and statistical adaptive speckle filtering in SAR images. International Journal of Remote Sensing, 14(9), 1735–1758.

    Article  Google Scholar 

  32. Takeda, H., Farsiu, S., & Milanfar, P. (2007). Kernel regression for image processing and reconstruction. IEEE Transactions on Image Processing, 16(2), 349–366.

    Article  MathSciNet  Google Scholar 

  33. Dong, W., Zhang, L., & Shi, G. (2013). Nonlocally centralized sparse representation for image restoration. IEEE Transactions on Image Processing, 22(4), 1620–1630.

    Article  MathSciNet  Google Scholar 

  34. Krattenthaler, W., Mayer, K. J., & Zeiler, M. (1994). Point correlation: A reduced-cost template matching technique. In Proceedings of ICIP (pp. 208–212). Austin, TX.

  35. Dong, W., Shi, G., & Li, X. (2013). Nonlocal image restoration with bilateral variance estimation: A low-rank approach. IEEE Transactions on Image Processing, 22(2), 700–711.

    Article  MathSciNet  Google Scholar 

  36. Stewart, G. (1993). On the early history of the singular value decomposition. SIAM Review, 35(4), 551–566.

    Article  MathSciNet  MATH  Google Scholar 

  37. Yu, Y., & Acton, S. T. (2002). Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11), 1260–1270.

    Article  MathSciNet  Google Scholar 

  38. Wang, Z., 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.

    Article  Google Scholar 

  39. Martino, G. D., Poderico, M., Poggi, G., Riccio, D., & Verdoliva, L. (2014). Benchmarking framework for SAR despeckling. IEEE Transactions on Geoscience and Remote Sensing, 52(3), 1596–1615.

    Article  Google Scholar 

  40. Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank the Free Software Foundation Inc. for providing the Matlab code of the SRAD filter, C. Deledalle for opening the code of the PPB filter and S. Parrilli for providing the code of the SAR-BM3D filter. This paper is supported by the National Natural Science Fund of China for Distinguished Young Scholars (No. 61325007) and the National Natural Science Fund of China for International Cooperation and Exchanges (No. 61520106001)

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Ting Lu, Shutao Li & Leyuan Fang

  2. Faculty of Electrical and Computer Engineering, University of Iceland, 101, Reykjavík, Iceland

    Jón Atli Benediktsson

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  1. Ting Lu
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  2. Shutao Li
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  3. Leyuan Fang
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  4. Jón Atli Benediktsson
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Correspondence to Shutao Li.

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Lu, T., Li, S., Fang, L. et al. SAR Image Despeckling Via Structural Sparse Representation. Sens Imaging 17, 2 (2016). https://doi.org/10.1007/s11220-015-0127-y

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  • DOI: https://doi.org/10.1007/s11220-015-0127-y

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