An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49708", author = "D.Gnanadurai and V.Sadasivam", title = "An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising", 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.", volume = "2", number = "8", pages = "1566-6", }