OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 32 — Nov. 10, 2010
  • pp: 6286–6294

Satellite image deconvolution based on nonlocal means

Ming Zhao, Wei Zhang, Zhile Wang, and Qingyu Hou  »View Author Affiliations

Applied Optics, Vol. 49, Issue 32, pp. 6286-6294 (2010)

View Full Text Article

Enhanced HTML    Acrobat PDF (1006 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized under the Bayesian framework by introducing an appropriate image prior. In this paper, we derive a new image prior based on the state-of-the-art nonlocal means (NLM) denoising approach under Markov random field theory. Inheriting from the NLM, the prior exploits the intrinsic high redundancy of satellite images and is able to encode the image’s nonsmooth information. Using this prior, we propose an inhomogeneous deconvolution technique for satellite images, termed nonlocal means-based deconvolution (NLM-D). Moreover, in order to make our NLM-D unsupervised, we apply the L-curve approach to estimate the optimal regularization parameter. Experimentally, NLM-D demonstrates its capacity to preserve the image’s nonsmooth structures (such as edges and textures) and outperforms the existing total variation-based and wavelet-based deconvolution methods in terms of both visual quality and signal-to-noise ratio performance.

© 2010 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.1830) Image processing : Deconvolution
(100.3010) Image processing : Image reconstruction techniques
(100.3020) Image processing : Image reconstruction-restoration

ToC Category:
Image Processing

Original Manuscript: May 18, 2010
Revised Manuscript: September 23, 2010
Manuscript Accepted: September 24, 2010
Published: November 5, 2010

Ming Zhao, Wei Zhang, Zhile Wang, and Qingyu Hou, "Satellite image deconvolution based on nonlocal means," Appl. Opt. 49, 6286-6294 (2010)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. J. Idier, “Convex half-quadratic criteria and interacting auxiliary variables for image restoration,” IEEE Trans. Image Process. 10, 1001–1009 (2001). [CrossRef]
  2. S. Teboul, L. Blanc-Feraud, G. Aubert, and M. Barlaud, “Variational approach for edge-preserving regularization using coupled PDE’s,” IEEE Trans. Image Process. 7, 387–397 (1998). [CrossRef]
  3. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D (Amsterdam) 60, 259–268 (1992). [CrossRef]
  4. J. P. Oliveira, J. M. Bioucas-Dias, and M. A. T. Figueiredo, “Total variation-based image deconvolution: A majorization-minimization approach,” Signal Process. 89, 1683–1693(2009). [CrossRef]
  5. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul. 4, 490–530 (2005). [CrossRef]
  6. G. Gilboa and S. Osher, “Nonlocal linear image regularization and supervised segmentation,” Multiscale Model. Simul. 6, 595–630 (2007). [CrossRef]
  7. M. Protter, M. Elad, H. Takeda, and P. Milanfar, “Generalizing the non-local-means to super-resolution reconstruction,” IEEE Trans. Image Process. 18, 36–51 (2009). [CrossRef]
  8. A. Buades, B. Coll, and J. Morel, Image Enhancement by Non-Local Reverse Heat Equation (CMLA, 2006).
  9. Y. Lou, X. Zhang, S. Osher, and A. Bertozzi, “Image recovery via nonlocal operators,” J. Sci. Comput. 42, 185–197 (2009). [CrossRef]
  10. W. Zhang, M. Zhao, and Z. Wang, “Adaptive wavelet-based deconvolution method for remote sensing imaging,” Appl. Opt. 48, 4785–4793 (2009). [CrossRef] [PubMed]
  11. M. Mignotte, “A non-local regularization strategy for image deconvolution,” Pattern Recogn. Lett. 29, 2206–2212(2008). [CrossRef]
  12. X. Zhang, M. Burger, X. Bresson, and S. Osher, “Bregmanized nonlocal regularization for deconvolution and sparse reconstruction,” SIAM J. Imaging Sci 3, 253–276 (2010). [CrossRef]
  13. R. Pan and S. J. Reeves, “Efficient Huber-Markov edge-preserving image restoration,” IEEE Trans. Image Process. 15, 3728–3735 (2006). [CrossRef] [PubMed]
  14. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6, 721–741 (1984). [CrossRef]
  15. C. Bouman and K. Sauer, “A generalized Gaussian image model for edge-preserving MAP estimation,” IEEE Trans. Image Process. 2, 296–310 (1993). [CrossRef] [PubMed]
  16. G. H. Golub, M. Heath, and G. Wahba, “Generalized cross-validation as a method for choosing a good ridge parameter,” Technometrics 21, 215–223 (1979). [CrossRef]
  17. G. Golub and U. Matt, “Generalized cross-validation for large scale problems,” J. Comput. Graph. Stat. 6, 1–34 (1997). [CrossRef]
  18. H. Liao, F. Li, and M. K. Ng, “Selection of regularization parameter in total variation image restoration,” J. Opt. Soc. Am. A 26, 2311–2320 (2009). [CrossRef]
  19. A. Jalobeanu, L. Blanc-FeHraud, and J. Zerubia, “Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method,” Pattern Recogn. 35, 341–352 (2002). [CrossRef]
  20. A. Jalobeanu, L. Blanc-Féraud, and J. Zerubia, “An adaptive Gaussian model for satellite image deblurring,” IEEE Trans. Image Process. 13, 613–621 (2004). [CrossRef] [PubMed]
  21. R. Molina, A. K. Katsaggelos, and J. Mateos, “Bayesian and regularization methods for hyperparameter estimation in image restoration,” IEEE Trans. Image Process. 8, 231–246(1999). [CrossRef]
  22. S. D. Babacan, R. Molina, and A. K. Katsaggelos, “Parameter estimation in TV image restoration using variational distribution approximation,” IEEE Trans. Image Process. 17, 326–339 (2008). [CrossRef] [PubMed]
  23. P. C. Hansen and D. P. O’Leary, “The use of the L-curve in the regularization of discrete ill-posed problems,” SIAM J. Sci. Comput. 14, 1487–1503 (1993). [CrossRef]
  24. G. Rodriguez and D. Theis, “An algorithm for estimating the optimal regularization parameter by the L-curve,” Rendiconti di matematica 25, 69–84 (2005).
  25. P. C. Hansen, T. K. Jensen, and G. Rodriguez, “An adaptive pruning algorithm for the discrete L-curve criterion,” J. Comput. Appl. Math. 198, 483–492 (2007). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited