Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 6153749, 14 pages
http://dx.doi.org/10.1155/2016/6153749
Research Article

Neural Architectures for Correlated Noise Removal in Image Processing

Computer Science Department, Bucharest University of Economics, 010552 Bucharest, Romania

Received 21 January 2016; Accepted 24 March 2016

Academic Editor: Marco Perez-Cisneros

Copyright © 2016 Cătălina Cocianu and Alexandru Stan. 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. W. Pratt, Digital Image Processing, Wiley-Interscience, Hoboken, NJ, USA, 4th edition, 2007.
  2. E. López-Rubio, “Restoration of images corrupted by Gaussian and uniform impulsive noise,” Pattern Recognition, vol. 43, no. 5, pp. 1835–1846, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. L. State, C. Cocianu, C. Săraru, and P. Vlamos, “New approaches in image compression and noise removal,” in Proceedings of the 1st International Conference on Advances in Satellite and Space Communications (SPACOMM '09), pp. 96–101, IEEE, Colmar, France, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. H. Shamsi and D.-G. Kim, “Multiscale hybrid nonlocal means filtering using modified similarity measure,” Mathematical Problems in Engineering, vol. 2015, Article ID 318341, 17 pages, 2015. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Fieguth, Statistical Image Processing and Multidimensional Modeling, Springer, New York, NY, USA, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  6. L. Tan and J. Jiang, Digital Signal Processing. Fundamentals and Applications, Academic Press, Elsevier, 2nd edition, 2013.
  7. S. Kay, Fundamentals of Statistical Signal Processing, Volume III: Practical Algorithm Development, Prentice Hall, New York, NY, USA, 2013.
  8. M. Egmont-Petersen, D. de Ridder, and H. Handels, “Image processing with neural networks—a review,” Pattern Recognition, vol. 35, no. 10, pp. 2279–2301, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. F. Hussain and J. Jeong, “Efficient deep neural network for digital image compression employing rectified linear neurons,” Journal of Sensors, vol. 2016, Article ID 3184840, 7 pages, 2016. View at Publisher · View at Google Scholar
  10. A. J. Hussain, D. Al-Jumeily, N. Radi, and P. Lisboa, “Hybrid neural network predictive-wavelet image compression system,” Neurocomputing, vol. 151, no. 3, pp. 975–984, 2015. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Bhattacharyya, P. Pal, and S. Bhowmick, “Binary image denoising using a quantum multilayer self organizing neural network,” Applied Soft Computing Journal, vol. 24, pp. 717–729, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Li, J. Lu, L. Wang, and Y. Takashi, “Denoising by using multineural networks for medical X-ray imaging applications,” Neurocomputing, vol. 72, no. 13–15, pp. 2884–2891, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. I. Turkmen, “The ANN based detector to remove random-valued impulse noise in images,” Journal of Visual Communication and Image Representation, vol. 34, pp. 28–36, 2016. View at Publisher · View at Google Scholar
  14. S. Haykin, Neural Networks A Comprehensive Foundation, Prentice Hall, 1999.
  15. Y. Wu, B. H. Tracey, P. Natarajan, and J. P. Noonan, “Fast blockwise SURE shrinkage for image denoising,” Signal Processing, vol. 103, pp. 45–59, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis, John Wiley & Sons, New York, NY, USA, 2001.
  17. L. Shang, D.-S. Huang, C.-H. Zheng, and Z.-L. Sun, “Noise removal using a novel non-negative sparse coding shrinkage technique,” Neurocomputing, vol. 69, no. 7–9, pp. 874–877, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. N. N. Popomarenko, V. V. Lukin, A. A. Zelensky, J. T. Astola, and J. T. Astola, “Adaptive DCT-based filtering of images corrupted by spatially correlated noise,” in Image Processing: Algorithms and Systems VI, vol. 6812 of Proceedings of SPIE, San Jose, Calif, USA, January 2008. View at Publisher · View at Google Scholar
  19. N. N. Popomarenko, V. V. Lukin, K. O. Egiazarian, and J. T. Astola, “A method for blind estimation of spatially correlated noise characteristics,” in Image Processing: Algorithms and Systems VIII, vol. 7532 of Proceedings of SPIE, February 2010. View at Publisher · View at Google Scholar
  20. I. M. Johnstone and B. W. Silverman, “Wavelet threshold estimators for data with correlated noise,” Journal of the Royal Statistical Society. Series B. Methodological, vol. 59, no. 2, pp. 319–351, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  21. J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of Gaussians in the wavelet domain,” IEEE Transactions on Image Processing, vol. 12, no. 11, pp. 1338–1351, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  22. A. Pižurica and W. Philips, “Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising,” IEEE Transactions on Image Processing, vol. 15, no. 3, pp. 654–665, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Goossens, Q. Luong, A. Pizurica, and W. Philips, “An improved non-local denoising algorithm,” in Proceedings of the International Workshop on Local and Non-Local Approximation in Image Processing (LNLA '08), pp. 143–156, Lausanne, Switzerland, August 2008.
  24. M. Jansen, Noise Reduction by Wavelet Thresholding, Springer, Berlin, Germany, 2001. View at Publisher · View at Google Scholar · View at MathSciNet
  25. 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 MathSciNet · View at Scopus
  26. M. P. S. Chawla, “PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: a survey and comparison,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2216–2226, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Griffanti, G. Salimi-Khorshidi, C. F. Beckmann et al., “ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging,” NeuroImage, vol. 95, pp. 232–247, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Common and C. Jutten, Handbook of Blind Source Separation: Independent Component Analysis and Applications, Academic Press, Elsevier, 2010.
  29. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 2nd edition, 1990. View at MathSciNet
  30. J. E. Gentle, Matrix Algebra. Theory, Computations, and Applications in Statistics, Springer Texts in Statistics, Springer, New York, NY, USA, 2007. View at Publisher · View at Google Scholar · View at MathSciNet
  31. I. T. Jolliffe, Principal Component Analysis, Springer Series in Statistics, Springer, Berlin, Germany, 2nd edition, 2002. View at MathSciNet
  32. C. Cocianu, L. State, and P. Vlamos, “Neural implementation of a class of PCA learning algorithms,” Economic Computation and Economic Cybernetics Studies and Research no. 3/2009, 2009. View at Google Scholar
  33. K. Gnana Sheela and S. N. Deepa, “Review on methods to fix number of hidden neurons in neural networks,” Mathematical Problems in Engineering, vol. 2013, Article ID 425740, 11 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. G.-B. Huang, “Learning capability and storage capacity of two-hidden-layer feedforward networks,” IEEE Transactions on Neural Networks, vol. 14, no. 2, pp. 274–281, 2003. View at Publisher · View at Google Scholar · View at Scopus
  35. G. A. F. Seber and C. J. Wild, Nonlinear Regression, John Wiley & Sons, New York, NY, USA, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  36. R. Gonzales and R. Woods, Digital Image Processing, Prentice Hall, 5th edition, 2008.
  37. 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
  38. http://www.geocities.ws/senthilirtt/Senthil%20Face%20Database%20Version1.
  39. http://sipi.usc.edu/database/database.php?volume=sequences.
  40. A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denoising,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 2, pp. 60–65, IEEE, San Diego, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus