International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.9, No.5, May. 2017

Combination of Spatial Filtering and Adaptive Wavelet Thresholding for Image Denoising

Full Text (PDF, 1077KB), PP.9-19

Views:73   Downloads:2


Abdelhak Bouhali, Daoud Berkani

Index Terms

2D-DWT;adaptive thresholding;image denoising;JBF;spatial filtering


Thresholding in wavelet domain has proven very high performances in image denoising and particularly for homogeneous ones. Conversely, and in cases of relatively non-homogeneous scenes, it often induces the loss of some true coefficients; inducing so, to smoothing the details and the different features of the thresholded image. Therefore, and in order to overcome this shortcoming, we introduce within this paper a new alternative made by a combination of advantages of both spatial filtering and wavelet thresholding; that ensures well removing the noise effect while preserving the different features of the considered image. First, the degraded image is decomposed into wavelet coefficients via a 2-level 2D-DWT. Then, the finest detail sub-bands likely due to noise, are thresholded in order to maximally cancel the noise contribution. The remaining noise shared across the coarse detail subbands (LH2, HL2, and HH2) is cleaned by filtering these mentioned sub-bands via an adaptive wiener filter instead of thresholding them; avoiding so smoothing the acquired image. Finally, a joint bilateral filter (JBF) is applied to ensure the preservation of the different image features. Experimental results show notable performances of our new proposed scheme compared to the recent state-of-the-art schemes visually and in terms of (MSE), (PSNR) and correlation coefficient.

Cite This Paper

Abdelhak Bouhali, Daoud Berkani,"Combination of Spatial Filtering and Adaptive Wavelet Thresholding for Image Denoising", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.5, pp.9-19, 2017.DOI: 10.5815/ijigsp.2017.05.02


[1]A. Bouhali and D. Berkani, “Combinaison du Filtrage Linéaire et le Seuillage Adaptatif d’Ondelettes pour le Dé-bruitage d’Images Satellitaires,” 3ème Conférence Internationale sur la Vision Artificielle. Tizi-Ouzou, pp. 34–41, April 2015.

[2]B. J. Yoon and P. P. Vaidyanathan, “Wavelet-Based Denoising by Customized Thresholding,” ICASSP, pp. II-925–II-928, 2004.

[3]S. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. 

[4]S. Mallat, A Wavelet Tour of Signal Processing, 3rd ed., The Sparse Way. Academic Press, 2008.

[5]S. Mallat and W. L. Hwang, “Singularity Detection and Processing with Wavelets,” IEEE Trans. on Information Theory. Vol. 38, pp. 617–643, 1992.

[6]I. Daubechies, Ten Lectures on Wavelets, Proc. CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia, vol. 61, 1992. 

[7]D. L. Donoho, “De-Noising by Soft-Thresholding,” IEEE Trans. on Information Theory, vol.41, pp. 613–627, 1995.

[8]D. Zhigang, Z. Jingxuan and J. Chunrong, “An Improved Wavelet Threshold Denoising Algorithm,” 2013 Third International Conference on Intelligent System Design and Engineering Applications, pp. 297–299, 2013.

[9]A. Fathi and A. R. N. Nilchi, “Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function,” IEEE Trans. on Image Processing, vol. 21, pp. 3981–3990, 2012.

[10]Y. Norouzzadeh and M. Rashidi, “Image Denoising in Wavelet Domain Using a New Thresholding function,” International Conference on Information Science and Technology, pp. 721–724, Nanjing, 2011.

[11]S. G. Chang, B. Yu and M. Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression,” IEEE Transactions on Image Processing, vol. 9, pp. 1532–1546, 2000.

[12]D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaptation by wavelet shrinkage,” Biometrika, vol. 81, pp. 425–455, 1994.

[13]I. M. Johnstone and B. W. Silverman, “Wavelet Threshold Estimators for Data with Correlated Noise,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 59, pp. 319–351, 1997.

[14]D. L. Donoho, I. M. Johnstone, G. Kerkyacharian and D. Picard, “Density Estimation by Wavelet Thresholding,” the Annals of Statistics, vol. 24, pp. 508–539, 1996.

[15]D. L. Donoho and I. M. Johnstone, “Minimax Estimation via Wavelet Shrinkage,” The Annals of Statistics, vol. 26, pp. 879–921, 1998.

[16]H. Y. Gao, “Wavelet shrinkage denoising using the nonnegative garrote,” J. Comput. Graph. Statist., vol. 7, pp. 469-488, 1998.

[17]H. T. Fang and D. S. Huang, “Wavelet Denoising by Means of Trimmed Thresholding,” Proceedings of the 5th World Congress on Intelligent Control and Automation, pp. 15-19, Hangzhou, 2004.

[18]A. Dixit, P. Sharma, “A Comparative Study of Wavelet Thresholding for Image Denoising”, IJIGSP, vol.6, no.12, pp.39-46, 2014.DOI: 10.5815/ijigsp.2014.12.06.

[19]S. Usha, S. Kuppuswami, “Performance Analysis of Fingerprint Denoising Using Stationary Wavelet Transfrom,” IJIGSP, vol.7, no.11, pp.48-54, 2015. DOI: 10.5815/ijigsp.2015.11.07.

[20]D. L. Donoho and I. M. Johnstone, “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of the American Statistical Association, vol. 90, pp. 1200–1224, 1995.

[21]I. Elyasi and S. Zarmehi, “Elimination Noise by Adaptive Wavelet Threshold,” World Academy of Science, Engineering and Technology, vol. 32, pp. 462–466, 2009.

[22]S. M. Hashemi and S. Beheshti, “Adaptive Image Denoising by Rigorous BayesShrink Thresholding,” 2011 IEEE Statistical Signal Processing Workshop, pp. 713–716, 2011.

[23]L. Kaur, S. Gupta and R.C.Chauhan, “Image denoising using wavelet thresholding,” ICVGIP, vol. 2, pp. 16-18, 2002.

[24]B. C. Rao and M. Latha, “Reconfigurable Wavelet Thresholding for Image Denoising while Keeping Edge Detection,” International Journal of Computer Science and Network Security, vol. 11, pp. 222–226, 2011.

[25]D. Gnanadurai and V. Sadasivam, “An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising,” International Journal of Information and Communication Engineering, vol. 2, pp. 114–119, 2006.

[26]Y. Rajput, V. S. Rajput, A. Thakur and G. Vyas, “Advanced Image Enhancement Based on Wavelet & Histogram Equalization for Medical Images,” IOSR Journal of Electronics and Communication Engineering, vol. 2, pp. 12–16, (2012).

[27]V. C. Bibina and S. Viswasom, “Adaptive Wavelet Thresholding & Joint Bilateral Filtering for Image Denoising,” Annual IEEE India Conference, pp. 1100–1104, 2012.

[28]V. Dawar and M. Bansal, “Denoising of Image Using Least Minimum Mean Square Error,” International Journal of Engineering and Advanced Technology, vol. 2, pp. 69–73, 2012.

[29]M. Vijay and L. S. Devi, “Image Denoising by Multiscale - LMMSE in Wavelet Domain and Joint Bilateral Filter in Spatial Domain,” International Journal of Soft Computing and Engineering, vol. 2, pp. 411–416, 2012.

[30]M. Vijay and S. V. Subha, “Spatially adaptive Image Restoration Method Using LPG-PCA and JBF,” International Conference on Machine Vision and Image Processing, pp. 53–56, 2012. 

[31]S. Arivazhagan, N. Sugitha and M. Vijay, “A New Hybrid Image Restoration Method Based on Fusion of Spatial and Transform Domain Methods,” International Conference on Recent Advances in computing and Software, pp. 48–53, 2012.

[32]M. Misiti, Y. Misiti, G. Oppenheim and J. M. Poggi, Wavelets and Their Applications, John Wiley & Sons 2013.

[33]J. S. Lim, Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice-Hall, 1990. 

[34]M. Kumar, M. Diwakar, “A New Locally Adaptive Patch Variation Based CT Image Denoising”, IJIGSP, Vol.8, No.1, pp.43-50, 2016.DOI: 10.5815/ijigsp.2016.01.05.

[35]Matlab Image Processing toolbox.

[36]C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” Proceedings of the IEEE International Conference on Computer Vision, pp. 839–846, 1998.

[37]S. Paris, P. Kornprobst, J. Tumblin and F. Durand, “Bilateral Filtering : Theory and Applications,” Foundation and Trends in Computer Graphics and Vision, vol. 4, pp. 1–73, 2009.

[38]H. Yu, L. Zhao and H. Wang, “Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain,” IEEE Transactions on Image Processing, vol. 18, pp. 2364–2369, 2009.