Abstract
In this paper, A contourlet domain SAR image de-speckling algorithm via self-snake diffusion and sparse representation theory is presented in order to reduce the influence of the SAR image speckle noise on the large-scale target edge information of the low frequency subband and the texture information of the high frequency subband. For this algorithm, firstly, the contourlet transform is applied to the speckled SAR image, adjusts the directional number of each dimension to represent SAR image in the high dimensional space. Then, the low frequency subband without sparsity is filtered by self-snake diffusion and the filtered coefficient is regarded as the local average estimate of the low-frequency subband in the contourlet domain. Sparse representation optimization model of SAR image is presented for suppressing the speckle noise of the high frequency subbands with sparsity, and solves sparse coefficients of the high frequency subbands by using the improved orthogonal matching pursuit algorithm. Finally, the de-speckled image is reconstructed from all of the filtered subband coefficients by the inverse contourlet transform. This paper simulates three representative experiments and the experimental results demonstrate that the proposed algorithm has a better de-speckling performance with preserving the edge of the SAR image.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Achim A, Tsakalides P, Bezerianos A (2003) SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed model. IEEE Trans Geosci Remote Sens 41(8):1773–1784
Candes EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509
Candes EJ, Tao T (2006) Near-optimal signal recover from random projection: universal encoding strategies. IEEE Trans Inf Theory 52(12):5406–5425
Chen SB (2010) Research of de-speckling in SAR Image. Huazhong University of Science & Technology
Debotosh B (2014) Adaptive polar transform and fusion for human face image processing and evaluation. Hum Centric Comput Inf Sci 4(4):1–18
Foucher S (2008) SAR image filtering via learned dictionaries and sparse representations. IEEE Int Geosci Remote Sens Symp (IGARSS) 1(1):1229–1232
Ghimire D, Lee J (2014) Extreme learning machine ensemble using bagging for facial expression recognition. J Inf Process Syst 10(3):443–458
Goodman JW (1976) Some fundamental properties of speckle. J Opt Soc Am B 66(11):1145–1150
Hebar M, Gleich D, Cucej Z (2009) Autobinomial model for SAR image despeckling and information extraction. IEEE Trans Geosci Remote Sens 47(8):2818–2835
Ji XX, Zhang G (2013) An improved orthogonal matching pursuit algorithm for blind sparsity signal reconstruction. J Astronaut 34(8):1146–1151
Khalighi SP, Fatemeh et al (2014) Iris recognition using robust localization and nonsubsampled contourlet based features. J Signal Process Syst 6(24):14–21
Li HJ, Zhang JS (2010) Image denoising based on the sparse land using redundant bandelet transform. J China Railw Soc 32(5):108–113
Liu SQ, Hu SH, Xiao Y (2012) Shearlet domain SAR image de-noising via sparse representation. J Electron Inf Technol 34(9):2110–2115
Liu JL, Zuo BQ, Zeng XY (2014) The visual quality recognition of nonwovens using a novel wavelet based contourlet transform. Multimedia Tools Appl 70(3):1667–1684
Ourabia, Soumya (2013) SAR images noise-removal method using the stationary contourlet transform. 2013 8th International Workshop on Systems. Signal Processing and Their Applications. WoSSPA. pp 429–434
Patel VM, Nasrabadi NM, Chellappa R (2010) Sparsity inspired automatic target recognition. Proceedings of SPIE 2010
Tosic I, Olshausen BA, Culpepper BJ (2011) Learning sparse representations of depth. IEEE J Sel Top Sign Proces 5(5):941–952
Weickert J, Kühne G (2003) Fast methods for implicit active contour models U. Geometric level set methods in imaging, vision, and graphics. Part II: 43–57
Xu PF, Miao QG, Tang X et al (2014) A denoising algorithm via wiener filtering in the shearlet domain. Multimedia Tools Appl 71(3):1529–1558
Yang M, Gong Z (2012) SAR image de-speckling using over-complete dictionary. Electron Lett 48(10):596–597
Zhao RZ, Liu XY, Li CC (2009) Wavelet denoising via sparse representation. Sci China Ser F 52(8):1371–1377
Zhu L, Shui PL, Wu AJ. New algorithm for feducing speckle noise in the SAR image [J]. J Xidian Univ 39(2): 80–86
Acknowledgments
The authors would like to thank the anonymous reviewers for their helpful comments and advices which contributed much to the improvement of this paper. The work was jointly supported by the National Science Foundation of China under grant No. 61471191, 61071163, 61271327, the Natural Science Foundation of Jiangsu colleges and universities under grant No. 14KJD510004.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ji, X., Zhang, G. Contourlet domain SAR image de-speckling via self-snake diffusion and sparse representation. Multimed Tools Appl 76, 5873–5887 (2017). https://doi.org/10.1007/s11042-015-2560-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2560-2