An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising

Authors: D.Gnanadurai, V.Sadasivam

Abstract:

This frame work describes a computationally more efficient and adaptive threshold estimation method for image denoising in the wavelet domain based on Generalized Gaussian Distribution (GGD) modeling of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analysing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by the proposed method. Experimental results on several test images by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR). Here, to prove the efficiency of this method in image denoising, we have compared this with various denoising methods like wiener filter, Average filter, VisuShrink and BayesShrink.

Keywords: Wavelet Transform, Gaussian Noise, ImageDenoising, Filter Banks and Thresholding.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328672

References:

[1] Maher A. Sid-Ahmed. (1995). Image Processing-Theory algorithm and architecture. McGraw-Hill, pp 78-80.
[2] Rafael C.Gonzalez & Richard E.Wodds. (1993). Digital Image Processing. Addison Wesley publishing Company, pp 41-43.
[3] Javier Portilla, Vasily Strela, Martin J.Wainwright and Eero P. Simoncelli. (2002). Adaptive Wiener Denoising using a Gaussian Scale Mixture Model in the wavelet Domain. Proceedings of the 8th International Conference of Image Processing Thessaloniki, Greece.
[4] D.L. Donoho and I.M. Johnstone. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of American Statistical Association., Vol. 90, no. 432, pp1200-1224.
[5] S. Grace Chang, Bin Yu and M. Vattereli. (2000). Wavelet Thresholding for Multiple Noisy Image Copies. IEEE Transaction. Image Processing, vol. 9, pp.1631- 1635.
[6] S. Grace Chang, Bin Yu and M. Vattereli. (2000). Spatially Adaptive Wavelet Thresholding with Context Modeling for Imaged noising. IEEE Transaction - Image Processing, volume 9, pp. 1522-1530.
[7] M. Vattereli and J. Kovacevic. (1995). Wavelets and Subband Coding. Englewood Cliffs. NJ, Prentice Hall.
[8] Maarten Janse. (2001). Noise Reduction by Wavelet Thresholding. Volume 161, Springer Verlag, United States of America, I edition.
[9] Carl Taswell. (2000). The what, how and why wavelet shrinkage denoising. Computing in science and Engineering, pp.12-19.
[10] D.L. Donoho. (1994). Ideal spatial adoption by wavelet shrinkage. Biometrika, volume 81, pp.425-455.
[11] S. Grace Chang, Bin Yu and M. Vattereli. (2000). Adaptive Wavelet Thresholding for Image denoising and Compression. IEEE Transaction, Image Processing, vol. 9, pp. 1532-15460.
[12] Raghuveer M. Rao and Ajit. S. Bopardikar. (1998). Wavelet Transforms: Introduction to theory and applications". Addison Wesley Longman Inc, pp 151-166.
[13] D.L. Donoho. (1995). De-noising by soft thresholding. IEEE Transactions on Information Theory, volume 41, pp.613-627.
[14] I. Daubechies. (1992). Ten Lectures on Wavelets. Philadelphia SIAM.
[15] Amir Said. (1995). A New and Efficient Image Codec Based On Set Partitioning in Hierarchical Tress, IEEE Transaction on circuit and system for video technology, Volume 6, p 48.