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A New Image Denoising Scheme Using Soft-Thresholding

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DOI: 10.4236/jsip.2012.33046    5,721 Downloads   9,137 Views   Citations

ABSTRACT

The VisuShrink is one of the important image denoising methods. It however does not provide good quality of image due to removing too many coefficients especially using soft-thresholding technique. This paper proposes a new image denoising scheme using wavelet transformation. In this paper, we modify the coefficients using soft-thresholding method to enhance the visual quality of noisy image. The experimental results show that our proposed scheme has better performance than the VisuShrink in terms of peak signal-to-noise ratio (PSNR) i.e., visual quality of the image.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

H. Om and M. Biswas, "A New Image Denoising Scheme Using Soft-Thresholding," Journal of Signal and Information Processing, Vol. 3 No. 3, 2012, pp. 360-363. doi: 10.4236/jsip.2012.33046.

References

[1] M. Jansen, “Noise Reduction by Wavelet Thresholding,” Springer Verlag Inc., New York, 2001.
[2] J. Gao, H. Sultan, J. Hu and W. W. Tung, “Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: A Comparison,” IEEE Signal Processing Letters, Vol. 17, No. 3, 2010, pp. 237-240. HUdoi:10.1109/LSP.2009.2037773U
[3] A. Khademi, A. A. Venetsanopoulos and A. R. Moody “Image Enhancement and Noise Suppression for FLAIR MRIs with White Matter Lesions,” IEEE Signal Processing Letters, Vol. 17, No. 12, 2010, pp. 989-992. HUdoi:10.1109/LSP.2010.2082527U
[4] D. B. H. Tay “Daubechies Wavelets as Approximate Hilbert-Pairs?” IEEE Signal Processing Letters, Vol. 15, 2008, pp. 57-60. HUdoi:10.1109/LSP.2007.910318U
[5] D. L. Donoho, “De-Noising by Soft Thresholding,” IEEE Transactions on Information Theory, Vol. 41, No. 3, 1995, pp. 613-627. HUdoi:10.1109/18.382009U
[6] D. L. Donoho and I. M. Johnstone, “Adapting to Unknown Smoothness via Wavelet Shrinkage,” Journal of American Statistical Association, Vol. 90, No. 432, 1995, pp. 1200-1224. HUdoi:10.1080/01621459.1995.10476626U
[7] D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaptation via Wavelet Shrinkage,” Biometrika, Vol. 81, No. 3, 1994, pp. 425-455. HUdoi:10.1093/biomet/81.3.425U
[8] D. L. Donoho and I. M. Johnstone, “Wavelet Shrinkage: Asymptotic?” Journal of the Royal Statistical Society, Series B (Methodological), Vol. 57, No. 2, 1995, pp. 301- 369.
[9] C. S. Burrus, R. A. Gopinath and H. Guo, “Introduction to Wavelet and Wavelet Transforms: A Primer,” Prentice Hall, Upper Saddle River, 1998.
[10] M. Vattereli and J. Kovacevic, “Wavelets and Subband Coding,” Prentice Hall, Englewood Cliffs, 1995.
[11] H. Om and M. Biswas, “An Improved Image Denoising Method Based on Wavelet Thresholding,” Journal of Signal and Information Processing (USA), Vol. 3, No. 1, 2012, pp. 109-116. HUdoi:10.4236/jsip.2012.31014U
[12] H. Om and M. Biswas, “An Enhanced Image Denoising Scheme,” International Journal of Advanced Computer Engineering, Review, 2012.
[13] H. Q. Li and S. Q. Wang, “A New Image Denoising Method Using Wavelet Transform,” International Forum on Information Technology and Applications, Chengdu, 15-17 May 2009, pp. 111-114.
[14] S. Gupta and L. Kaur, “Wavelet Based Image Compression using Daubechies Filters,” Proceeding of 8th National Conference on Communications, I.I.T., Bombay NCC, 2002.
[15] I. Daubechies, “Ten Lectures on Wavelets,” Proceeding of CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM, Philadelphia, 1992.

  
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