Abstract

This study proposes novel image denoising algorithm using combination method. This method combines both Wavelet Based Denoising (WBD) and Principle Component Analysis (PCA) to increase the superiority of the observed image, subjectively and objectively. We exploit the important property of second generation WBD and PCA to increase the performance of our designed filter. One of the main advantages of the second generation wavelet transformation in noise reduction is its ability to keep the signal energy in small amount of coefficients in the wavelet domain. On the other hand, one of the main features of PCA is that the energy of the signal concentrates on a very few subclasses in PCA domain, while the noise’s energy equally spreads over the entire signal; this characteristic helps us to isolate the noise perfectly. Our algorithm compares favorably against several state-of-the- art filtering systems algorithms, such as Contourlet soft thresholding, Scale mixture by WT, Sparse 3D transformation and Normal shrink. In addition, the combined algorithm achieves very competitive performance compared with the traditional algorithms, especially when it comes to investigating the problem of how to preserve the fine structure of the tested image and in terms of the computational complexity reduction as well.

Keywords:

Cycle spinning , execution time , image quality , PSNR , wavelet based denoised,

References

1. Abry, P., R. Baraniuk, P. Flandrin, R. Riedi and D. Veitch, 2002. The multiscale nature of network traffic: Discovery, analysis and modeling. IEEE Signal Proc. Mag., 19(3): 28-46.
   CrossRef    
2. Asem, K., A. Ramli, S. Al-Haddad and S. Hashim, 2014. Additive and multiplicative noise removal based on adaptive wavelet transformation using cycle spinning. Am. J Appl. Sci., 11 (2): 316-328.
   CrossRef    
3. Bakshi, B., 1999. Multiscale analysis and modeling using wavelets. J. Chemometr., 13(4): 415-434.
   CrossRef    
4. Chan, T.F. and J. Shen, 2005. Image Processing and Analysis: Variational, PDE, Wavelet and Stochastic Methods. Society for Industrial and Applied Mathematics, Philadelphia.
   CrossRef    
5. Donoho, D. and I. Johnstone, 1998. Minimax estimation via wavelet shrinkage. Ann. Stat., 26: 879-921.
   CrossRef    
6. Donoho, D.L., I.M. Johnstone, G. Kerkyacharian and D. Picard 1995. Wavelet shrinkage: Asymptopia? J. Roy. Stat. Soc. B Met., 57: 301-369.
   
7. Goldstein, D.E., O.V. Vasilyev, A.A. Wray and R.S. Rogallo, 2000. Evaluation of the use of second generation wavelets in the coherent vortex simulation approach. Proceedings of the Summer Program Center for Turbulence Research, pp: 293-304.
   
8. Gruber, P., F.J. Theis, K. Stadlthanner and E.W. Lang, 2004. Denoising using local ICA and kernel-PCA. Proceedings IEEE International Joint Conference on Neural Networks, 3: 2071-2076.
   CrossRef    
9. Gupta, V., C. Vikas, C. Chan, C. Poh, T.H. Chow, T.C. Meng and N.B. Koon, 2008. Computerized automation of wavelet based denoising method to reduce speckle noise in OCT images. Proceeding of 5th International Conference on Information Technology and Applications in Biomedicine (ITAB, 2008), pp: 120-123.
   CrossRef    
10. Hesamoddin, J., S. Hamid and H. Gholam Ali, 2005. Noise suppression of fMRI time-series in wavelet domain. Proceedings of the 7th IASTED International Conference on Signal and Image Processing (SIP, 2005), pp: 136-138.
    
11. Hyvarinen, A., J. Karhunen and E. Oja, 2011. Independent Component Analysis. John Wiley and Sons Inc., New York.
    
12. Jolliffe, I.T., 2004. Principal Component Analysis. Springer Science+Business Media, Inc., New York.
    
13. Liò, P., A.T. Lawniczak, S. Xie and J. Xu, 2008. Wavelet-domain statistics of packet switching networks near traffic congestion. In: Liò, P. et al. (Eds.), BIOWIRE 2007. LNCS 5151, Springer Verlag, Berlin, Heidelberg, pp: 268-279.
    CrossRef    
14. Mohsen, G., 2004. Adaptive fractal and wavelet image denoising. Ph.D. Thesis, the University of Waterloo.
    
15. Muresan, D.D. and T.W. Parks, 2003. Adaptive principal components and image denoising. Proceedings of International Conference on Image Processing, 1: I101-I104.
    CrossRef    
16. Percival, D.P. and A.T. Walden, 2000. Wavelet Methods for Time Series Analysis. Cambridge University Press, New York.
    CrossRef    
17. Raanan, F., 2009. A Brief Introduction to First-and Second-generation Wavelets. An Auxiliary Material for the ACM SIGGRAPH Paper: Edge-Avoiding Wavelets and their Applications.
    
18. Starck, J.L., F. Murtagh and A. Bijaoui, 1998. Image Processing and Data Analysis: The Multiscale Approach. Cambridge University Press, Cambridge.
    CrossRef    
19. Suganthy, M. and P. Ramamoorthy, 2012. Principal component analysis based feature extraction, morphological edge detection and localization for fast iris recognition. J. Comput. Sci., 8: 1428-1433.
    CrossRef    
20. Wang, Z., A.C. Bovik, H.R. Sheikh and E.P. Simoncelli, 2004. Image quality assessment: From error visibility to structural similarity. IEEE T. Image Process., 13(4): 600-612.
    CrossRef    
21. Weeks, M., 2006. Digital Signal Processing Using MATLAB and Wavelets. Infinity Science Press, 25.
    
22. Wink, A.M. and J.B. Roerdink, 2004. Denoising functional MR images: A comparison of wavelet denoising and Gaussian smoothing. IEEE T. Med. Imaging, 23(3): 374-387.
    CrossRef    
23. Yasmin, M., M. Sharif, S. Masood, M. Raza and S. Mohsin, 2012. Brain image enhancement-a survey. World Appl. Sci. J., 17: 1192-1204.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.