Speckle Denoising With NL Filter and Stochastic Distances Under the Haar Wavelet Domain
Resumo
Synthetic aperture radar SAR imaging systems have a coherent processing that causes the appearance of the multiplicative speckle noise. This noise gives a granular appearance to the terrestrial surface scene impairing its interpretation. The similarity between patches approach is applied by the current state-of-the-art filters in remote sensing area. The goal of this manuscript is to present a method to transform the non-local means (NLM) algorithm capable to mitigate the noise. Singlelook speckle and the NLM under the Haar wavelet domain are considered in our research with intensity SAR images. To achieve our goal, we used the Exponential-Polynomial (EP) and Gamma distributions to describe the Haar coefficients. Also, stochastic distances based on these two mentioned distributions were formulated and embedded in the original NLM technique. Finally, we present analyses and comparisons of real scenarios to demonstrate the competitive performance of the proposed method with some recent filters of the literature.
Referências
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H. H. Arsenault and G. April, “Properties of speckle integrated with a finite aperture and logarithmically transformed,” J. Opt. Soc. Am., vol. 66, no. 11, pp. 1160–1163, November 1976. https://doi.org/10.1364/JOSA.66.001160
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P. Coupé, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Transactions on Image Processing, vol. 18, no. 10, pp. 2221–2229, 2009. https://doi.org/10.1109/TIP.2009.2024064
A. Nath, “Image denoising algorithms: A comparative study of different filtration approaches used in image restoration,” in 2013 International Conference on Communication Systems and Network Technologies (CSNT), 2013, pp. 157–163. https://doi.org/10.1109/CSNT.2013.43
C. A. Deledalle, L. Denis, S. Tabti, and F. Tupin, “Mulog, or how to apply gaussian denoisers to multi-channel sar speckle reduction?” IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4389–4403, September 2017. https://doi.org/10.1109/TIP.2017.2713946
P. A. Penna and N. D. Mascarenhas, “(Non-) homomorphic approaches to denoise intensity sar images with non-local means and stochastic distances,” Computers & Geosciences, vol. 111, no. Supplement C, pp. 127 – 138, 2018. https://doi.org/10.1016/j.cageo.2017.11.006
F. Argenti, A. Lapini, T. Bianchi, and L. Alparone, “A tutorial on speckle reduction in synthetic aperture radar images,” IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 3, pp. 6–35, 2013. https://doi.org/10.1109/MGRS.2013.2277512
A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithm, with a new one,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490–530, 2005. https://doi.org/10.1137/040616024
K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-d transform-domain collaborative filtering,” IEEE Transactions on Image Processing, vol. 16, no. 8, pp. 2080–2095, August 2007. https://doi.org/10.1109/TIP.2007.901238
M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. https://doi.org/10.1109/TSP.2006.881199
A. Efros and T. Leung, “Texture synthesis by non parametric sampling,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 1033–1038, 1999. https://doi.org/10.1109/ICCV.1999.790383
C. Deledalle, L. Denis, and F. Tupin, “Iterative weighted maximum likelihood denoising with probabilistic patch-based weights,” IEEE Transactions on Image Processing, vol. 18, no. 12, pp. 2661–2672, December 2009. https://doi.org/10.1109/TIP.2009.2029593
C. Deledalle, L. Denis, F. Tupin, A. Reigber, and M. Jäger, “Nl-sar: A unified nonlocal framework for resolution-preserving (pol)(in)sar denoising,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 4, pp. 2021–2038, April 2015. https://doi.org/10.1109/TGRS.2014.2352555
S. Parrilli, M. Poderico, C. Angelino, and L. Verdoliva, “A nonlocal sar image denoising algorithm based on llmmse wavelet shrinkage,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 2, pp. 606–616, February 2012. https://doi.org/10.1109/TGRS.2011.2161586
D. Cozzolino, S. Parrilli, G. Scarpa, G. Poggi, and L. Verdoliva, “Fast adaptive nonlocal sar despeckling,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 99, pp. 1–5, 2013. https://doi.org/10.1109/LGRS.2013.2271650
A. D. C. Nascimento, R. J. Cintra, and A. C. Frery, “Hypothesis testing in speckled data with stochastic distances,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 1, pp. 373–385, January 2010. https://doi.org/10.1109/TGRS.2009.2025498
P. A. A. Penna and N. D. A. Mascarenhas, “Sar speckle nonlocal filtering with statistical modeling of Haar wavelet coefficients and stochastic distances,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 7194–7208, September 2019. https://doi.org/10.1109/TGRS.2019.2912153
L. Torres, T. Cavalcante, and A. C. Frery, “Speckle reduction using stochastic distances,” in Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, ser. Lecture Notes in Computer Science, L. Alvarez, M. Mejail, L. Gomez, and J. Jacobo, Eds., vol. 7441. Springer Berlin Heidelberg, 2012, pp. 632–639. https://doi.org/10.1007/978-3-642-33275-3_78
L. Torres and A. C. Frery, “Sar image despeckling algorithms using stochastic distances and nonlocal means,” in Workshop of Theses and Dissertations (WTD) in SIBGRAPI 2013 (XXVI Conference on Graphics, Patterns and Images), Arequipa, Peru, August 2013.
L. Torres, S. J. Sant’Anna, C. da Costa Freitas, and A. C. Frery, “Speckle reduction in polarimetric {SAR} imagery with stochastic distances and nonlocal means,” Pattern Recognition, vol. 47, no. 1, pp. 141 – 157, 2014. https://doi.org/10.1016/j.patcog.2013.04.001
C. A. N. Santos, D. L. N. Martins, and N. D. A. Mascarenhas, “Ultrasound image despeckling using stochastic distance-based BM3D,” IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2632–2643, June 2017. https://doi.org/10.1109/TIP.2017.2685339
C. A. N. Santos and N. D. A. Mascarenhas, “Geodesic distances in probabilistic spaces for patch-based ultrasound image processing,” IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 216–226, January 2019. https://doi.org/10.1109/TIP.2018.2866705
C. A. Santos and N. D. Mascarenhas, “Patch similarity in ultrasound images with hypothesis testing and stochastic distances,” Computerized Medical Imaging and Graphics, 2019. https://doi.org/10.1016/j.compmedimag.2019.03.001
A. A. Bindilatti and N. D. A. Mascarenhas, “A nonlocal poisson denoising algorithm based on stochastic distances,” IEEE Signal Processing Letters, vol. 20, no. 11, pp. 1010–1013, November 2013. https://doi.org/10.1109/LSP.2013.2277111
A. A. Bindilatti, M. A. Vieira, and N. D. Mascarenhas, “Poisson Wiener filtering with non-local weighted parameter estimation using stochastic distances,” Signal Processing, vol. 144, no. Supplement C, pp. 68 – 76, 2018. https://doi.org/10.1016/j.sigpro.2017.10.001
S. Mallat, “A theory for multiresolution signal decomposition: the wavelet representation,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 11, no. 7, pp. 674–693, July 1989. https://doi.org/10.1109/34.192463
A. Misra, B. Kartikeyan, and S. Garg, “Wavelet based sar data denoising and analysis,” in Advance Computing Conference (IACC), 2014 IEEE International, February 2014, pp. 1087–1092. https://doi.org/10.1109/IAdCC.2014.6779477
R. S. Stankovic and B. J. Falkowski, “The haar wavelet transform: its status and achievements,” Computers & Electrical Engineering, vol. 29, no. 1, pp. 25 – 44, 2003. https://doi.org/10.1016/S0045-7906(01)00011-8
A. K. Chan and C. Peng, Wavelet for sensing technologies. Boston : Artech House, 2003.
J. W. Goodman, “Some fundamental properties of speckle∗,” J. Opt. Soc. Am., vol. 66, no. 11, pp. 1145–1150, November 1976. https://doi.org/10.1364/JOSA.66.001145
H. H. Arsenault and G. April, “Properties of speckle integrated with a finite aperture and logarithmically transformed,” J. Opt. Soc. Am., vol. 66, no. 11, pp. 1160–1163, November 1976. https://doi.org/10.1364/JOSA.66.001160
E. S. Ribeiro, “Novas propostas em filtragem de projeções tomográficas sob ruı́do poisson,” Master’s thesis, Federal University of São Carlos - Computing Department,, 2010.
B. Klar, “A note on gamma difference distributions,” Journal of Statistical Computation and Simulation, vol. 85, no. 18, pp. 3708– 3715, 2015. [Online]. Available: http://dx.doi.org/10.1080/00949655.2014.996566
E. Krishna and K. Jose, “Marshall-olkin generalized asymmetric laplace distributions and processes,” Statistica, vol. 71, no. 4, pp. 453–467, 2011. https://doi.org/10.6092/issn.1973-2201/3627
M. Salicru, D. Morales, M. Menendez, and L. Pardo, “On the applications of divergence type measures in testing statistical hypotheses,” Journal of Multivariate Analysis, vol. 51, no. 2, pp. 372 – 391, 1994. https://doi.org/10.1006/jmva.1994.1068
P. Coupé, P. Hellier, C. Kervrann, and C. Barillot, “Nonlocal means-based speckle filtering for ultrasound images,” IEEE Transactions on Image Processing, vol. 18, no. 10, pp. 2221–2229, 2009. https://doi.org/10.1109/TIP.2009.2024064
Publicado
28/10/2019
Como Citar
PENNA, Pedro A. A.; MASCARENHAS, Nelson D. A..
Speckle Denoising With NL Filter and Stochastic Distances Under the Haar Wavelet Domain. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 32. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 91-97.
DOI: https://doi.org/10.5753/sibgrapi.est.2019.8307.
