Abstract
Transform-based denoising methods are very popular in recent years. However, they often suffer from unwanted artifacts like pesudo-Gibbs phenomena. In this paper, we propose a new hybrid image denoising by combining the nonsubsumpled contourlet transform (NSCT) with improved total variation. First, an improved stark function which integrates noise reduction with feature enhancement is developed to nonlinearly shrink and stretch the NSCT coefficients. Then an improved Total variation is introduced to reduce the pseudo-Gibbs artifacts of the enhanced image which are caused by the elimination of small NSCT coefficients. Numerical experiments show that this approach improves the image quality by enhancing the shape of edges and important detailed features while suppressing noise in comparison to many well known methods.
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References
Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)
Do, M.N., Vetterli, M.: Contourlets: A directional multiresolution image representation. In: Proc. IEEE Int. Conf. Image Processing (2002)
Cunha, A.L., Zhou, J., Do, M.N.: ‘The nonsubsampled contourlet transform: theory, design and applications. IEEE Trans. Image Process. 15(10), 3089–3101 (2006)
Alvarez, L., Guichard, F., Lions, P.-L., Morel, J.-M.: Axioms and fundamental equations of image processing. Arch. Rational Mech. Anal. 123, 199–257 (1993)
Catte, F., Lions, P., Morel, M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(3), 182–193 (1992)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Rudin, L., Osher, S.: Total variation based image restoration with free local constraints. In: Proc. 1st IEEE Int. Conf. Image Processing, vol. 1, pp. 31–35 (1994)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Rudin, L., Osher, S.: Total variation based image restoration with free local constraints. In: Proc. 1st IEEE Int. Conf. Image Processing, vol. 1, pp. 31–35 (1994)
Gilboa, G., Sochen, N., Zeevi, Y.: Variational denoising of partly textured images by spatially varying constraints. IEEE Trans. Image Process. 15(8), 2281–2289 (2006)
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Li, Y., Jia, Y., Zhang, Y. (2013). A New Image Denoising and Enhancement Method Combining the Nonsubsampled Contourlet Transform and Improved Total Variation. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_108
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DOI: https://doi.org/10.1007/978-3-642-42057-3_108
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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