A wavelet-based denoising method for color image of mobile phone

In modern society, people's requirement of mobile phone camera is higher and higher, its image denoising method is also becoming the focus of attention. In this paper, starting from the actual requirements of mobile phone image denoising, an adaptive mobile phone image denoising method based on bivariate shrinkage function was proposed. Firstly, the actual image noise was made similar to the Gaussian noise by down sampling process, and then a kind of adaptive noise variance of bivariate shrinkage function based on Bayesian denoising method was put forward to estimate variance of the down sampled image. Finally, the denoised image was grayed to further eliminate the noise in the flat areas. Simulation and actual experimental results showed that the method of this paper can get better denoising effect compared with the existing mainstream methods.

[1]  R. Chartrand,et al.  Total variation regularisation of images corrupted by non-Gaussian noise using a quasi-Newton method , 2008 .

[2]  Moongu Jeon,et al.  Multilevel adaptive thresholding and shrinkage technique for denoising using Daubechies complex wavelet transform , 2010 .

[3]  H. Hassanpour,et al.  Using diffusion equations for improving performance of wavelet-based image denoising techniques , 2010 .

[4]  Mario Greco,et al.  Improved anisotropic diffusion filtering for sar image despeckling , 2013 .

[5]  Ahmed S. Fahmy,et al.  Background noise removal in cardiac magnetic resonance images using bayes classifier , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Cai Liangshi,et al.  Image denoising based on correlation of wavelet coefficients , 2012 .

[7]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[8]  Hu Bo,et al.  Survey of Dual-Tree Complex Wavelet Transform and Its Applications , 2007 .

[9]  Levent Sendur,et al.  Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency , 2002, IEEE Trans. Signal Process..

[10]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[11]  Fei Shumin,et al.  A difference curvature controlled non-local means method for noise reduction , 2014 .

[12]  Wang Xi-yuan.  Complex wavelet domain image denoising based on Bayesian technique , 2005 .

[13]  X. Tan,et al.  A local structural adaptive partial differential equation for image denoising , 2013, Multimedia Tools and Applications.

[14]  Wang Weihong,et al.  A Method Using Nonparametric Hidden Markov Trees for Image Denoising , 2009, 2009 International Conference on Computational Intelligence and Natural Computing.

[15]  NI Hong-xia,et al.  De-noising Method Based on Adaptive Wavelet Thresholding , 2005 .

[16]  Dan Schonfeld,et al.  A General Two-Dimensional Hidden Markov Model and its Application in Image Classification , 2007, 2007 IEEE International Conference on Image Processing.

[17]  Nick G. Kingsbury,et al.  The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[18]  N. Kingsbury.  Image processing with complex wavelets , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[19]  Ting-Li Chen,et al.  A Markov Random Field Model for Medical Image Denoising , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[20]  Giovanni Motta,et al.  The iDUDE Framework for Grayscale Image Denoising , 2011, IEEE Transactions on Image Processing.

[21]  W. Zeng,et al.  Region-based non-local means algorithm for noise removal , 2011 .