Abstract
In this paper, we propose a Selective Parameters based Image Denoising method that uses a shrinkage parameter for each coefficient in the subband at the corresponding decomposition level. Image decomposition is done using the wavelet transform. VisuShrink, SureShrink, and BayesShrink define good thresholds for removing the noise from an image. SureShrink and BayesShrink denoising methods depend on subband to evaluate the threshold value whereas the VisuShrink is a global thresholding method. These methods remove too many coefficients and do not provide good visual quality of the image. Our proposed method not only keeps more noiseless coefficients but also modifies the noisy coefficients using the threshold value. We experimentally show that our method provides better performance in terms of objective and subjective criteria i.e. visual quality of image than the VisuShrink, SureShrink, and BayesShrink.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jansen, M.: Noise Reduction by Wavelet Thresholding. Springer, New York (2001)
Xie, J.C.: Overview on Wavelet Image Denoising. Journal of Image and Graphic 7(3), 209–217 (2002)
Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994)
Donoho, D.L., Johnstone, I.M.: Wavelet shrinkage: Asymptotic? J. R. Stat. Soc. B 57(2), 301–369 (1995)
Donoho, D.L., Johnstone, I.M.: Adapting to Unknown Smoothness via Wavelet Shrinkage. Journal of American Statistical Association 90(432), 1200–1224 (1995)
Donoho, D.L.: De-Noising by Soft Thresholding. IEEE Trans. Info. Theory 41(3), 613–627 (1995)
Chang, S.G., Yu, B., Vetterli, M.: Adaptive Wavelet Thresholding for Image De-noising and Compression. IEEE Trans. Image Processing 9(9), 1532–1546 (2000)
Elyasi, I., Zarmehi, S.: Elimination Noise by Adaptive Wavelet Threshold. World Academy of Science, Engineering and Technology, 462–466 (2009)
Weeks, M., Bayoumi, M.: Discrete Wavelet Transform: Architectures, Design and Performance Issues. Journal of VLSI Signal Processing 35(2), 155–178 (2003)
Daubechies, I.: The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Transaction on Information Theory 36(5), 961–1005 (1990)
Yang, Y., Wei, Y.: Neighboring Coefficients Preservation for Signal Denoising. Circuits, Systems, and Signal Processing 31(2), 827–832 (2012)
Om, H., Biswas, M.: An Improved Image Denoising Method based on Wavelet Thresholding. Journal of Signal and Information Processing 3(1), 109–116 (2012)
Chen, G., Zhu, W.-P.: Image Denoising Using Neighbouring Contourlet Coefficients. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds.) ISNN 2008, Part II. LNCS, vol. 5264, pp. 384–391. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Biswas, M., Om, H. (2013). Selective Parameters Based Image Denoising Method. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-32063-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
eBook Packages: EngineeringEngineering (R0)