NSCT De-Noising Algorithm Based on Image Partition and Noise Variance Estimation

Abstract:

Aiming at the low de-noising effect of texture detail in the image, a NSCT de-noising method based on image partition and noise variance estimation was proposed. Since NSCT has the features of translation invariance and multi-directional selectivity, and noise has different impacts on different textures of image, the image can be divided into several blocks with the same size, then the noise variance and threshold of each block will be calculated, furthermore, NSCT is used to denoise each block, and finally, the blocks are merged. The experiments proved that by comparison with traditional NSCT de-noising algorithm, the proposed algorithm effectively preserved the texture detail information of original image.

• Periodical: Applied Mechanics and Materials (Volumes 58-60)
• Pages: 1842-1846
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.58-60.1842
• Online since: June 2011
• Authors: Yue Liu, Yi Bing Li, Yong Xu
• Keywords: Image De-Noising, Image Partition, Noise Variance Estimation, Nonsubsampled Contourlet Yransform (NSCT)

References

[1] S. Mallat, A Wavelet Tour of Signal Processing, Academic press, SanDiego (1998).

[2] E. J. Candès and D. L. Donoho, Ridgelets: A key to higher-dimensional intermittency? Phil. Trans. R. Soc. Lond. A (1999) 2495-2509.

[3] E. J. Candès and D. L. Donoho, New tight frames of curvelets and optimal representations of objects with piecewise singularities, Communications on Pure and Applied Mathematics 57(2) (2004) 219-266.

DOI: 10.1002/cpa.10116

[4] Do M N and Vetterli M, The contourlet transform: An efficient directional multiresolution image representation, IEEE Transactions on Image Processing 14(12) (2005) 2091-2106.

DOI: 10.1109/tip.2005.859376

[5] F. G. Meyer and R. R. Coifman, Brushlets: A tool for directional image analysis and image compression, Applied and computational Harmonic Analysis 4(2) (1997) 147-187.

DOI: 10.1006/acha.1997.0208

[6] Cunha A L da, Zhou J P and Do M N, The nonsubsampled Contourlet transform: Theory, design and application, IEEE Transactions on Image Processing 15(10) (2006) 3089-3101.

DOI: 10.1109/tip.2006.877507

[7] Bian Ce, Zhong Hua and Jiao Licheng, Image de-noising based on nonsubsampled contourlet transform and bivariate model, Journal of Electronics & Information Technology 31(3) (2009) 561-565.

[8] Wu Xiaoyue, Guo Baolong and Li Leida, A new image de-noising method based on nonsubsampled contourlet transform, Optoelectronics Laser 20(5) (2009) 657-661.

DOI: 10.3788/aos20092908.2147

[9] Gilboa G, Sochen N and Zeevi Y, Variational de-noising of partly textured images by spatially varying constraints, IEEE Transactions on Image Processing 15(8) (2006) 2281-2289.

DOI: 10.1109/tip.2006.875247