A New Image De-Noising Algorithm Based on Wavelet Transform

Abstract:

This paper proposed a new image de-noising algorithm based on wavelet transform. Firstly, the algorithm made wavelet transform on the image, and then using the GGD described the wavelet coefficients of each sub band. Calculate the similarity of direction of horizontal, vertical and diagonal. Then adjust the coefficients according to similarity function. The experiment results showed that the algorithm not only remove the noise from the image but can protect the edge information of the image. The processing result had better visual effect and high signal to noise ratio.

• Periodical: Applied Mechanics and Materials (Volumes 182-183)
• Pages: 1816-1820
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.182-183.1816
• Online since: June 2012
• Authors: Zhi Qiang Wang, Hui Shu An, Kai Cai Zhao, Yan Liang
• Keywords: GGD, Image De-Noising, Wavelet Transform (WT)

References

[1] Perona P, Malik J. "Scale-space and edge detection using anisotropic diffusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, val.7 , December 1990, pp.629-639.

DOI: 10.1109/34.56205

[2] Tomasi C, Manduchi R. "Bilateral filtering for gray and color images," Proc. Of the 1998 IEEE Inter, Conf. on Computer Vision, Bombay, India, 1998, pp.839-846.

DOI: 10.1109/iccv.1998.710815

[3] Lindenbaum M, Fischer M, Bruckstein A, "On Gabor's contribution to image enhancement," Pattern Recognition, val. 1, 1994, pp.1-8.

[4] L.Rudin, S.Osher, E.Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, val. 60, 1992, pp.259-268.

DOI: 10.1016/0167-2789(92)90242-f

[5] Donoho D L, Johnstone I M, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, val. 3, 1994, pp.425-455.

DOI: 10.1093/biomet/81.3.425

[6] Weyrich N, Warhola G T, "Wavelet shrinkage and generalized cross validation for image denoising," IEEE Transactions on Image Processing, val 1, July 1998, pp.82-90.

DOI: 10.1109/83.650852

[7] Jansen M, Bultheel A, "Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise," IEEE Transactions on Image Processing, val. 7, August 1999, pp.947-953.

DOI: 10.1109/83.772237

[8] Souidene W, Beghdadi A, Abed-Mdraim K, "Image denoising in the transformed domain using non local neighborhoods," Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, 2006,pp.869-872.

DOI: 10.1109/icassp.2006.1660481

[9] Mahmoudi M, Sapiro G, "Fast image and video denoising via nonlocal means of similar neighborhoods," IEEE Signal Processing Letters, val 12, December 2005, pp.839-842.

DOI: 10.1109/lsp.2005.859509

[10] M.N.Do, M.Vetterli, "Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance," IEEE Transactions on Image Processing, val 2, November 2002, pp.146-158.

DOI: 10.1109/83.982822

[11] K.Sharifi, A.Leon-Garcia, "Estimation of shape parameter for generalized Gaussian distributions in subband decompositions of video," IEEE Transactions on Circuits Syst and Video Technology, val. 5, 1995, pp.52-56.

DOI: 10.1109/76.350779