Abstract
Based on the key observation that the coding residuals between the recovered sparse codes of the noisy SAR image and those of the clean SAR image are sparse, we propose a sparse representation-based despeckling algorithm for SAR image. As the sparse codes of the clean SAR image are not available, the rich nonlocal repetitive structures of the logarithmic SAR images are exploited. To collect the similar patches within the logarithmic SAR image, an adaptive similarity evaluation obeying statistical distribution of the logarithmic speckle noise is derived. Experimental results on both synthetic and real SAR images demonstrate the validity of the proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
M. Aharon, M. Elad, A.M. Bruckstein, The K-SVD: An algorithm for designing of overcomplete dictionaries for sparse representations. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
A. Buades, B. Coll, J.M. Morel, A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005)
K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
I. Daubechies, M. Defriese, C. DeMol, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)
C.A. Deledalle, L. Denis, F. Tupin, Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Process. 18(12), 2661–2672 (2009)
C.A. Deledalle, L. Denis, F. Tupin, How to compare noisy patches? Patch similarity beyond Gaussian noise. Int. J. Comput. Vis. 99(1), 86–102 (2012)
W. Dong, X. Li, L. Zhang, G. Shi, Sparsity-based image denoising via dictionary learning and structure clustering, in Proc. IEEE conference on computer vision and pattern recognition (CVPR), pp. 457–464, (2011)
W. Dong, L. Zhang, G. Shi, X. Wu, Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization. IEEE Trans. Image Process. 20(7), 1838–1857 (2011)
W. Dong, L. Zhang, G. Shi, X. Li, Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2012)
D. Donoho, De-noising by soft thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)
M. Elad, M. Aharon, Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)
S. Foucher, SAR image filtering via learned dictionaries and sparse representations, in Proc. IEEE Int. Geosci. Remote Sens. Symp., (2008), vol. 1, pp. 229-232
V.S. Frost, J.A. Stiles, A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 4(2), 157–166 (1982)
D. Gleich, M. Datcu, Gauss-Markov model for wavelet-based SAR image despeckling. IEEE Signal Process. Lett. 13(6), 365–368 (2006)
D. Gleich, M. Datcu, Wavelet-based despeckling of SAR images using Gauss-Markov random fields. IEEE Trans. Geosci. Remote Sens. 45(12), 4127–4143 (2007)
J. Jiang, L. Jiang, N. Sang, Non-local sparse models for SAR image despeckling, in Proc. IEEE Int. Conf. Comput. Vis. Remote Sens., (2012), pp. 230–236
D.T. Kuan, A.A. Sawchuk, T.C. Strand, P. Chavel, Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell. 7(2), 165–177 (1985)
J.S. Lee, Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2(2), 165–168 (1980)
J.S. Lee, J.H. Wen, T.L. Ainsworth, K.S. Chen, A.J. Chen, Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans. Geosci. Remote Sens. 47(1), 202–213 (2009)
A. Lope, E. Nezry, R. Touzi, H. Laur, Maximum a posteriori filtering and first order texture models in SAR images, in Proc IGARSS, Washington, DC, (1990), pp. 2409–2412
A. Lope, R. Touzi, E. Nezry, Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosci. Remote Scenes. 28(6), 992–1000 (1990)
C. Ozcan, B. Sen, F. Nar, Sparsity driven despeckling for SAR images. IEEE Geosci. Remote Sens. Lett. 13(1), 115–119 (2016)
S. Parrilli, M. Poderico, C.V. Angelino, L. Verdoliva, A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50(2), 606–616 (2012)
Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
H. Xie, L.E. Pierce, F.T. Ulaby, Statistical properties of logarithmically transformed speckle. IEEE Trans. Geosci. Remote Sens. 40(3), 721–727 (2002)
B. Xu, Y. Cui, Z. Li, B. Zuo, J. Yang, J. Song, Patch ordering-based SAR image despeckling via transform-domain filtering. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 8(4), 1682–1695 (2015)
B. Xu, Y. Cui, Z. Li, J. Yang, An iterative SAR image filtering method using nonlocal sparse model. IEEE Geosci. Remote Sens. Lett. 12(8), 1635–1639 (2015)
M. Yang, G. Zhang, SAR image despeckling using overcomplete dictionary. Electron. Lett. 48(10), 596–597 (2012)
S.K. Zhou, R. Chellappa, From sample similarity to ensemble similarity: probabilistic distance measures in reproducing kernel Hilbert space. IEEE Trans. Pattern Anal. Mach. Intell. 28(6), 917–929 (2006)
Acknowledgements
Funding was provided by National Key Laboratory of Science (Grand No: 85456233).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chen, S., Gao, L. & Li, Q. SAR Image Despeckling by Using Nonlocal Sparse Coding Model. Circuits Syst Signal Process 37, 3023–3045 (2018). https://doi.org/10.1007/s00034-017-0704-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-017-0704-5