2020 Vol.3 Jun. No.3

• Title: Face Image Denoising Based on Sharp Frequency Localization Contourlet
• Name: Zhang chi
• Company: Department of Computer, Hunan City University, Yiyang 413000, Ch
• Abstract:

  Face Image denoising is very important in to image processing, howerver, conventional thresholding shrinkage for denoising is designed with assumption that the coefficients in transformation domain are independent.  In this paper, we proposed a novel method for face image denoising by exploring the dependencies among the coefficients. The method considered three corresponding coefficients, including the noisy coefficient, its parent coefficient and its neighbor coefficient based on the Sharp Frequency Localization Contourlet, and established a trivariate distribution model to estimate the latent coefficient. Furthermore, the shrinkage function in model is derived under the Bayesian framework. The experiment results showed that the performance of proposed method outperformed the current denoising method.

• Keyword: Face image; Contourlet; Image denoising; Statistical distribution; Shrinkage function;
• DOI: 10.12250/jpciams2020030108
• Citation form: Zhang chi.Face Image Denoising Based on Sharp Frequency Localization Contourlet[J]. Computer Informatization and Mechanical System, 2020, vol. 3, pp. 123-127.

[1]M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006,  15(12),  3736–3745.
[2]A. Buades, B. Coll, J.-M. Morel. A non-local algorithm for image denoising. International Conference on Computer Vision and Pattern Recognition, 2005, 2, pp. 60-65.
[3]K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian. Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Transactions on Image Processing, 2007, 16(8), 2080-2095,.
[4]A. Beck, M. Teboulle. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processessing, 2009, 18(11), 2419-2434.
[5]W. Dong, L. Zhang, G. Shi, X. Wu. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization.[J] IEEE Trans. on Image Processing, 2011, 20(7), pp. 1838-1857.
[6]A. N. Tikhonov. Solution of incorrectly formulated problems and regularization method. In Soviet Math. Dokl., 1963, vol. 4, 1035-1038, 1963.
[7]J. Oliveira, J. M. Bioucas-Dias, M. Figueiredo. Adaptive total variation image deblurring: a majorization-minimization approach. Signal Processing, 2009, 89(9), 1683-1693.
[8]J. Mairal, M. Elad, G. Sapiro. Sparse Representation for Color Image Restoration. IEEE Transactions on Image Processing, 2008, 17(1), 53-69.
[9]A. M. Bruckstein, D. L. Donoho, M. Elad. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 2009, 51(1), 34-81.
[10]I. Daubechies, M. Defrise, C. De Mol. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.[J] Communications on Pure and Applied Mathematics, 2004, 57(11), 1413-1457.
[11]P. Combettes, V. Wajs. Signal recovery by proximal forward-backward splitting. SIAM Journal of Multiscale and Model Simulations, 2005, 4, 1168-1200.
[12]Donoho.D.L. Denoising By Soft threshold. IEEE Transactions on Information Theory, 1995, 41(3), 613-627.
[13]Levent Sendur, Ivan W. Selesnick. Bivariate Shrinkage Functions for Wavelet-Based Denoising Exploiting Interscale Dependency. IEEE Transactions on Signal Processing, 2002, 50(11), 2744-2756.
[14]M. N. Do, M. Vetterli. The contourlet transform: an efficient directional multiresolution image representation. [J] IEEE Transactions Image on Processing, 2005, 14(12), 2091-2106,.
Yue Lu, Minh N. Do. A New Contourlet Transform with Sharp Frequency Localization International Conference on Image Processing, 2006, pp. 1629-1632.