Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

DOI: 10.4236/jsip.2013.41004   PDF   HTML   XML   7,502 Downloads   11,785 Views   Citations

Abstract

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

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A. Al Jumah, "Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques," Journal of Signal and Information Processing, Vol. 4 No. 1, 2013, pp. 33-41. doi: 10.4236/jsip.2013.41004.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] S. Mallat, “A Wavelet Tour of Signal Processing,” Cambridge University Press, New York, 1999.
[2] V. Strela and A. T. Walden, “Signal and Image Denoising via Wavelet Thresholding: Orthogonal and Biorthogonal, Scalar and Multiple Wavelet Transforms,” Statistic Section Technical Report TR-98-01, Department of Mathematics, Imperial College, London, 1998.
[3] D. L. Donoho and I. M. Johnstone, “Denoising by Soft Thresholding,” IEEE Transactions on Information Theory, Vol. 41, No. 3, 1995, pp. 613-627. doi:10.1109/18.382009
[4] S. G. Chang, B. Yu and M. Vattereli, “Wavelet Thresholding for Multiple Noisy Image Copies,” IEEE Transactions on Image Processing, Vol. 9, No. 9, 2000, pp.1631-1635. doi:10.1109/83.862646
[5] D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaptation by Wavelet Shrinkage,” Biometrika, Vol. 81, No. 3, 1994, pp. 425-455. doi:10.1093/biomet/81.3.425
[6] A. M. L. Lanzolla, G. Andria, F. Attivissimo, G. Cavone, M. Spadavecchia and T. Magli, “Denoising Filter to Improve the Quality of CT Images,” Proceedings of IEEE Conference on Instrumentation and Measurement Technology, Singapore City, 5-7 May 2009, pp. 947-950.
[7] S. Ruikar, Andria, D. D. Doye, “Image Denoising Using Wavelet Transform,” International Conference on Mechanical and Electrical Technology (ICMET 2010), Singapore City, 10-12 September 2010, pp. 509-515.
[8] I. Daubechies, “The Wavelet Transform, Time-Frequency Localization and Signal Analysis,” IEEE Transaction on Information Theory, Vol. 36, No. 5, 1990, pp. 961-1005.
[9] J. Portilla, V. Strela, M. Wainwright and E. Simoncelli, “Image Denoising Using Gaussian Scale Mixtures in the Wavelet Domain,” IEEE Transactions on Image Processing, Vol. 12, No. 11, 2003, pp. 1338-1351. doi:10.1109/TIP.2003.818640
[10] J. Portilla and E. P. Simoncelli, “Adaptive Wiener Denoising Using a Gaussian Scale Mixture Model in the Wavelet Domain,” IEEE International Conference on Image Process (ICIP), Vol. 2, 2001, pp. 37-40.
[11] S. G. Chang, B. Yu and M. Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression,” IEEE Transactions on Image Processing, Vol. 9, No. 9, 2000, pp. 1532-1546.
[12] A. Pizurica, W. Philips, I. Lemahieu and M. Acheroy, “A Versatile Wavelet Domain Noise Filtration Technique for Medical Imaging,” IEEE Transactions on Medical Imaging, Vol. 22, No. 3, 2003, pp. 323-331.
[13] M. K. Mihcak, I. Kozintsev, K. Ramchandran and P. Moulin, “Low-Complexity Image Denoising Based on Statistical Modelling of Wavelet Coefficients,” IEEE Signal Processing Letters, Vol. 6, No. 12, 1999, pp. 300-303. doi:10.1109/97.803428
[14] I. M. Johnstone and B. W. Silverman, “Wavelet Threshold Estimators for Data with Correlated Noise,” Journal of the Royal Statistical Society B, Vol. 59, No.2, 1997, pp. 319-351. doi:10.1111/1467-9868.00071
[15] F. Luisier, T. Blu and M. Unser, “A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding,” IEEE Transactions on Image Processing, Vol. 16, No. 3, 2007, pp. 593-606. doi:10.1109/TIP.2007.891064

  
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