Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for The Scientific World Journal, enter your email address in the box below.

The Scientific World Journal
Volume 2014, Article ID 826405, 15 pages
http://dx.doi.org/10.1155/2014/826405
Research Article

A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

Hilario Gómez-Moreno, Pedro Gil-Jiménez, Sergio Lafuente-Arroyo, Roberto López-Sastre, and Saturnino Maldonado-Bascón

Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares , 28805 Madrid, Spain

Received 2 May 2014; Revised 9 July 2014; Accepted 24 July 2014; Published 17 August 2014

Academic Editor: Gangyi Jiang

Copyright © 2014 Hilario Gómez-Moreno 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. Y.-H. Liu and Y.-T. Chen, “Face recognition using total margin-based adaptive fuzzy support vector machines,” IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 178–192, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Maldonado-Bascón, S. Lafuente-Arroyo, P. Gil-Jiménez, H. Gómez-Moreno, and F. López-Ferreras, “Road-sign detection and recognition based on support vector machines,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 2, pp. 264–278, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Fištrek and S. Lončarić, “Traffic sign detection and recognition using neural networks and histogram based selection of segmentation method,” in Proceedings of the 53rd International Symposium (ELMAR '11), pp. 51–54, September 2011. View at Scopus
  4. M. Rahman, P. Bhattacharya, and B. C. Desai, “A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback,” IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 1, pp. 58–69, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. E. Abreu, M. Lightstone, S. K. Mitra, and K. Arakawa, “A new efficient approach for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Image Processing, vol. 5, no. 6, pp. 1012–1025, 1996. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Akkoul, R. Lédée, R. Leconge, and R. Harba, “A new adaptive switching median filter,” IEEE Signal Processing Letters, vol. 17, no. 6, pp. 587–590, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Mélange, M. Nachtegael, and E. E. Kerre, “Fuzzy random impulse noise removal from color image sequences,” IEEE Transactions on Image Processing, vol. 20, no. 4, pp. 959–970, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. A. Toprak and I. Güler, “Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter,” Digital Signal Processing, vol. 17, no. 4, pp. 711–723, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. S. S. Alamri, N. V. Kalyankar, and S. D. Khamitkar, “A comparative study of removal noise from remote sensing image,” International Journal of Computer Science Issues, vol. 7, no. 1, pp. 32–36, 2010. View at Google Scholar
  10. R. Yang, L. Yin, M. Gabbouj, J. Astola, and Y. Neuvo, “Optimal weighted median filtering under structural constraints,” IEEE Transactions on Signal Processing, vol. 43, no. 3, pp. 591–604, 1995. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Chen and H. R. Wu, “Adaptive impulse detection using center-weighted median filters,” IEEE Signal Processing Letters, vol. 8, no. 1, pp. 1–3, 2001. View at Publisher · View at Google Scholar · View at Scopus
  12. 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
  13. J. Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering, Electronic Engineering Systems Series, CRC Press, New York, NY, USA, 1997.
  14. W.-Y. Han and J.-C. Lin, “Minimum-maximum exclusive mean (MMEM) filter to remove impulse noise from highly corrupted images,” Electronics Letters, vol. 33, no. 2, pp. 124–125, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Chen, K.-K. Ma, and L.-H. Chen, “Tri-state median filter for image denoising,” IEEE Transactions on Image Processing, vol. 8, no. 12, pp. 1834–1838, 1999. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Chang, J. Hsiao, and C. Hsieh, “An adaptive median filter for image denoising,” in Proceedings of the 2nd International Symposium on Intelligent Information Technology Application (IITA '08), vol. 2, pp. 346–350, Shanghai, China, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Wang and D. Zhang, “Progressive switching median filter for the removal of impulse noise from highly corrupted images,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 46, no. 1, pp. 78–80, 1999. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Hasan and F. Marvasti, “Efficient rank-ordered mean (ROM) techniques for the recovery of isolated losses in highly corrupted images,” IEEE Communications Letters, vol. 4, no. 10, pp. 321–322, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. T.-C. Lin, “A new adaptive center weighted median filter for suppressing impulsive noise in images,” Information Sciences, vol. 177, no. 4, pp. 1073–1087, 2007. View at Publisher · View at Google Scholar · View at Scopus
  20. K. S. Srinivasan and D. Ebenezer, “A new fast and efficient decision-based algorithm for removal of high-density impulse noises,” IEEE Signal Processing Letters, vol. 14, no. 3, pp. 189–192, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Russo and G. Ramponi, “A fuzzy filter for images corrupted by impulse noise,” IEEE Signal Processing Letters, vol. 3, no. 6, pp. 168–170, 1996. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Schulte, M. Nachtegael, V. de Witte, D. van der Weken, and E. E. Kerre, “A fuzzy impulse noise detection and reduction method,” IEEE Transactions on Image Processing, vol. 15, no. 5, pp. 1153–1162, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. K. Toh and N. Isa, “Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction,” IEEE Signal Processing Letters, vol. 17, no. 3, pp. 281–284, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Esakkirajan, T. Veerakumar, A. Subramanyam, and C. PremChand, “Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter,” IEEE Signal Processing Letters, vol. 18, no. 5, pp. 287–290, 2011. View at Google Scholar
  25. B. Smolka, R. Lukac, A. Chydzinski, K. N. Plataniotis, and W. Wojciechowski, “Fast adaptive similarity based impulsive noise reduction filter,” Real-Time Imaging, vol. 9, no. 4, pp. 261–276, 2003. View at Publisher · View at Google Scholar · View at Scopus
  26. L. Yin, J. Astola, and Y. Neuvo, “Neural filters: a class of filters unifying fir and median filters,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '92), vol. 4, pp. 53–56, April 1990. View at Scopus
  27. R. Sucher, “A recursive nonlinear filter for removal of impulse noise,” in Proceedings of the IEEE International Conference on Image Processing, vol. 1, pp. 183–186, October 1995. View at Scopus
  28. N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000.
  29. B. Schölkopf, K.-K. Sung, C. Burges et al., “Comparing support vector machines with gaussian kernels to radial basis function classifiers,” IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2758–2765, 1997. View at Publisher · View at Google Scholar · View at Scopus
  30. H. Gómez-Moreno, S. Maldonado-Bascón, F. López-Ferreras, M. Utrilla-Manso, and P. Gil-Jiménez, “A modified median filter for the removal of impulse noise based on the support vector machines,” in Advances in Signal Processing and Computer Technologies—WSEAS 2001, pp. 9–14, 2001. View at Google Scholar
  31. G. H. Gómez-Moreno, S. Maldonado-Bascón, F. López-Ferreras, and P. Gil-Jiménez, “Removal of impulse noise in images by means of the use of support vector machines,” in Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks (IWANN '03), pp. 536–543, Springer, New York, NY, USA, 2003. View at Google Scholar
  32. H. Gómez-Moreno, S. Maldonado-Bascón, and F. López-Ferreras, “Edge detection in noisy images using the support vector machines,” in Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks (WANN '01), vol. 2084 of Lecture Notes in Computer Science, pp. 685–692, Springer, 2001.
  33. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” Tech. Rep., University of Taiwan, 2001. View at Google Scholar
  34. 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
  35. F. Russo, “Noise removal from image data using recursive neurofuzzy filters,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 2, pp. 307–314, 2000. View at Publisher · View at Google Scholar · View at Scopus
  36. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998. View at MathSciNet
  37. M. Claesen, F. D. Smet, J. A. K. Suykens, and B. D. Moor, “Fast prediction with SVM models containing RBF kernels,” http://arxiv.org/abs/1403.0736.
  38. E. Alpaydin, Introduction to Machine Learning, The MIT Press, Boston, Mass, USA, 2nd edition, 2010.
  39. C. R. Souza, “Kernel functions for machine learning applications,” Tech. Rep., 2010, http://crsouza.blogspot.com.es/2010/03/kernel-functions-for-machinelearning.html?m=1. View at Google Scholar
  40. J. C. Platt, “Probabilities for SV machines,” in Advances in Large Margin Classifiers, P. Bartlett, B. Schölkopf, and D. Schuurmans, Eds., pp. 61–74, MIT Press, 1999. View at Google Scholar