Analysis the Statistical Parameters of the Wavelet Coefficients for Image Denoising
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Abstract
Abstract: Image denoising is aimed at the removal of noise which may corrupt an image during its acquisition or transmission. De-noising of the corrupted image by Gaussian noise using wavelet transform is very effective way because of its ability to capture the energy of a signal in few larger values. This paper proposes a threshold selection method for image de-noising based on the statistical parameters which depended on sub-band data. The threshold value is computed based on the number of coefficients in each scale j of wavelet decomposition and the noise variance in various sub-band. Experimental results in PSNR on several test images are compared for different de-noise techniques.
References
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[2] Motwani, M.C., Gadiya, M.C., Motwani, R.C., Harris, F.C Jr. “Survey of Image Denoising Techniques”.
[3] Windyga, S. P. 2001, “Fast Impulsive Noise Removal”, IEEE transactions on image processing, vol. 10, No. 1, pp. 173-178.
[4] Kailath, T. 1976, Equations of Wiener-Hopf type in filtering theory and related applications, in Norbert Wiener: Collected Works vol. III, P.Masani, Ed. Cambridge, MA: MIT Press, pp. 63–94.
[5] S.Grace Chang, Bin Yu and M.Vattereli, “Adaptive Wavelet Thresholding for Image Denoising and Compression”, IEEE Trans Image Processing, vol.9,pp.1532-1546, Sept 2000.
[6] D.L Donoho and I.M Johnstone, “Denoising by soft thresholding”, IEEE Trans on Inform Theory, vol 41, pp 613-627, 1995.
[7] F.Luisier, T. Blu and M. Unser, “A new SURE approach to image denoising: Inter-scale orthonormal wavelet thresholding”, IEEE Trans. Image Processing, vol 16, no.3, pp.593-606, Mar 2007.
[8] Iman Elyasi and Sadegh Zarmehi, “Elimination Noise by Adaptive Wavelet Threshold”, World Academy of Science, Engineering and Technology 32, 2009.