A New Image Denoising Scheme Using Soft-Thresholding

Hari Om, Mantosh Biswas

Indian School of Mines.

DOI: 10.4236/jsip.2012.33046                

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.

Keywords

Wavelet Thresholding; Image Denoising; Peak Signal-to-Noise Ratio

Conflicts of Interest

The authors declare no conflicts of interest.

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.