Elimination Noise by Adaptive Wavelet Threshold

Authors: Iman Elyasi, Sadegh Zarmehi

Abstract:

Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.

Keywords: Image denoising, Bayes Shrink, Modified Bayes Shrink, Normal Shrink.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071720

References:

[1] A.K.Katssagelous, Digital Image Restoration. New-York: springer-Verlag.1991.
[2] Lakhwinder Kaur and Savita Gupta and R.C.Chauhan, "Image denoising using wavelet thresholding,"punjab (148106), India.2003.
[3] S.ZHONG, and V.Cherkassy, "image denoising using wavelet thresholding and model selection,"proc.IEEE.Int.Conf.on image processing.
[ICIP].Vancouver.bc, Sept. 2000.
[4] Savita Gupta,"wavelet Based Image Compression Using Daubechies Filter," Bombay,NCC-2002.
[5] S. Grace Chang, "adaptive Wavelet Thresholding for Image denoising and compression," IEEE transactions on Image processing, Vol.9, No.9 september.2000.
[6] D.L.Donoho,"Denoising and soft thresholding," IEEE.transactions.information.Theory,VOL.41,PP.613-627,1995.
[7] D.L.Donoho,"nonlinear wavelet methods for recovery of signals, densities, and spectra from indirect and noisy data," in proc. of symposia in applied mathematics, VOL, 00, PP.173-205, AMS, 1993.
[8] S.G.Chang, B.YU, and martin Vetterli, "spatially adaptive wavelet thresholding with context modeling for image denoising," IEEE.trans.on image roc.,VOL.9,no.9,PP.1522-1531,2000.
[9] S.G.Chang, B.YU, and martin Vetterli, "bridging compression to wavelet thresholding as a denoising method," in proc. conf. information sciences systems, Baltimore, MD, PP.568-573, 1997.
[10] D.L.Donpho, and I.M.Johnstone, "adaptive to unknown smoothness via wavelet shrinkage," Journal of American statistical ASSOC.,VOL.90,NO.90,PP.1200-1224,1995.
[11] D.L.Donoho, and I.M.Johnstone, "Ideal spatial adaptation via wavelet shrinkage,"Biometrika,VOL.81,PP.425-455,1994.
[12] NM.Vatterli and J.Kovacevic, Wavelets and subband Coding. Englewood Cliffs, NJ, Prentice Hall, 1995.
[13] A.K. Katssagelous and K.T.Lay, "Maximum Likelihood Blur Identification and Image restoration Using the EM algorithm," IEEE Transactions on Signal Processing, Vol. 39, No.3, March 1991.