Table of Contents Author Guidelines Submit a Manuscript
Advances in Multimedia
Volume 2014, Article ID 682747, 14 pages
http://dx.doi.org/10.1155/2014/682747
Research Article

Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

1School of Computer and Information Engineering, Fujian Provincial University Key Laboratory of Internet of Things Application Technology, Xiamen University of Technology, Xiamen 361024, China
2Department of Electronic Science, Xiamen University, Xiamen 361005, China

Received 9 November 2013; Revised 30 January 2014; Accepted 30 January 2014; Published 13 March 2014

Academic Editor: Martin Reisslein

Copyright © 2014 Di Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. R. C. Gonzalez and E. Richard, Digital Image Processing, Prentice Hall, 2002.
  2. H. Hwang and R. A. Haddad, “Adaptive median filters: new algorithms and results,” IEEE Transactions on Image Processing, vol. 4, no. 4, pp. 499–502, 1995. View at Publisher · View at Google Scholar · View at Scopus
  3. B. Deka and S. Choudhury, “A multiscale detection based adaptive median filter for the removal of salt and pepper noise from highly corrupted images,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 6, pp. 129–144, 2013. View at Google Scholar
  4. P. S. J. Sree, P. Kumar, R. Siddavatam, and R. Verma, “Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets,” Signal, Image and Video Processing, vol. 7, pp. 111–118, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. M. H. Hsieh, F. C. Cheng, M. C. Shie, and S. J. Ruan, “Fast and efficient median filter for removing 1–99% levels of salt-and-pepper noise in images,” Engineering Applications of Artificial Intelligence, vol. 26, pp. 1333–1338, 2013. View at Google Scholar
  6. T.-C. Lin and Y. U. Pao-Ta, “Salt-pepper impulse noise detection and removal using multiple thresholds for image restoration,” Journal of Information Science and Engineering, vol. 22, no. 1, pp. 189–198, 2006. View at Google Scholar · View at Scopus
  7. R. Dharmarajan and K. Kannan, “A hypergraph-based algorithm for image restoration from salt and pepper noise,” International Journal of Electronics and Communications, vol. 64, no. 12, pp. 1114–1122, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Adeli, F. Tajeripoor, M. J. Zomorodian, and M. Neshat, “Comparison of the Fuzzy-based wavelet shrinkage image denoising techniques,” International Journal of Computer Science Issues, vol. 9, pp. 211–216, 2012. View at Google Scholar
  9. Y. Dong and S. Xu, “A new directional weighted median filter for removal of random-valued impulse noise,” IEEE Signal Processing Letters, vol. 14, no. 3, pp. 193–196, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. C.-T. Lu and T.-C. Chou, “Denoising of salt-and-pepper noise corrupted image using modified directional-weighted-median filter,” Pattern Recognition Letters, vol. 33, no. 10, pp. 1287–1295, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. K. K. V. Toh, H. Ibrahim, and M. N. Mahyuddin, “Salt-and-pepper noise detection and reduction using fuzzy switching median filter,” IEEE Transactions on Consumer Electronics, vol. 54, no. 4, pp. 1956–1961, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Huang and J. Zhu, “Removal of salt-and-pepper noise based on compressed sensing,” Electronics Letters, vol. 46, no. 17, pp. 1198–1199, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. X.-L. Wang, C.-L. Wang, J.-B. Zhu, and D.-N. Liang, “Salt-and-pepper noise removal based on image sparse representation,” Optical Engineering, vol. 50, no. 9, Article ID 097007, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. Q. Liu, S. Wang, J. Luo, Y. Zhu, and M. Ye, “An augmented Lagrangian approach to general dictionary learning for image denoising,” Journal of Visual Communication and Image Representation, vol. 23, pp. 753–766, 2012. View at Google Scholar
  15. E. Le Pennec and S. Mallat, “Sparse geometric image representations with bandelets,” IEEE Transactions on Image Processing, vol. 14, no. 4, pp. 423–438, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Qu, D. Guo, B. Ning et al., “Undersampled MRI reconstruction with patch-based directional wavelets,” Magnetic Resonance Imaging, vol. 30, pp. 964–977, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. B. Ning, X. Qu, D. Guo, C. Hu, and Z. Chen, “Magnetic resonance image reconstruction using trained geometric directions in 2D redundant wavelets domain and non-convex optimization,” Magnetic Resonance Imaging, vol. 31, pp. 1611–1622, 2013. View at Google Scholar
  18. 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. View at Publisher · View at Google Scholar · View at Scopus
  19. J.-F. Cai, R. H. Chan, and C. Di Fiore, “Minimization of a detail-preserving regularization functional for impulse noise removal,” Journal of Mathematical Imaging and Vision, vol. 29, no. 1, pp. 79–91, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Transactions on Image Processing, vol. 15, no. 12, pp. 3736–3745, 2006. View at Publisher · View at Google Scholar · View at Scopus
  21. J. F. Cai, H. Ji, Z. Shen, and G. B. Ye, “Data-driven tight frame construction and image denoising,” Applied and Computational Harmonic Analysis, 2013. View at Publisher · View at Google Scholar
  22. L. Ma, J. Yu, and T. Zeng, “Sparse representation prior and total variation-based image deblurring under impulse noise,” SIAM Journal on Imaging Sciences, vol. 6, pp. 2258–2284, 2013. View at Google Scholar
  23. X. Qu, Y. Hou, F. Lam, D. Guo, J. Zhong, and Z. Chen, “Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator,” Medical Image Analysis, 2013. View at Publisher · View at Google Scholar
  24. 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, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. Hou, C. Zhao, D. Yang, and Y. Cheng, “Comments on image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Transactions on Image Processing, vol. 20, no. 1, pp. 268–270, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. D. Huang, L. Kang, Y. Wang, and C. Lin, “Self-learning based image decomposition with applications to single image denoising,” IEEE Transactions on Multimedia, vol. 16, pp. 83–93, 2014. View at Google Scholar
  27. D. Guo, X. Qu, M. Wu, J. Yan, X. Chen, and K. Wu, “Impulse artefacts removal with similarity-motivated sparse representation,” submitted to Electronics Letters.
  28. R. H. Chan, C.-W. Ho, and M. Nikolova, “Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1479–1485, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Nikolova, “A variational approach to remove outliers and impulse noise,” Journal of Mathematical Imaging and Vision, vol. 20, no. 1-2, pp. 99–120, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. J.-F. Cai, R. H. Chan, and Z. Shen, “Simultaneous cartoon and texture Inpainting,” Inverse Problems and Imaging, vol. 4, no. 3, pp. 379–395, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. J.-L. Starck, M. Elad, and D. L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1570–1582, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123–151, 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Kwitt and A. Uhl, “Lightweight probabilistic texture retrieval,” IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 241–253, 2010. View at Publisher · View at Google Scholar · View at Scopus
  35. K. Bredies, K. Kunisch, and T. Pock, “Total generalized variation,” SIAM Journal on Imaging Sciences, vol. 3, no. 3, pp. 492–526, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 490–530, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. G. Peyre and S. Mallat, “Surface compression with geometric bandelets,” ACM Transactions on Graphics, vol. 24, pp. 601–608, 2005. View at Google Scholar
  38. J.-F. Cai, S. Osher, and Z. Shen, “Linearized Bregman iterations for compressed sensing,” Mathematics of Computation, vol. 78, no. 267, pp. 1515–1536, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM Journal on Imaging Sciences, vol. 2, pp. 323–343, 2009. View at Google Scholar
  40. W. Yin, S. Osher, D. Goldfarb, and J. Darbon, “Bregman iterative algorithms for l1-minimization with applications to compressed sensing,” SIAM Journal on Imaging Sciences, vol. 1, pp. 143–168, 2008. View at Google Scholar