Adaptive Kernel-Based Image Denoising Employing

[1]  Patrick L. Combettes,et al.  Image restoration subject to a total variation constraint , 2004, IEEE Transactions on Image Processing.

[2]  Jelena Kovacevic,et al.  Reproducible research in signal processing , 2009, IEEE Signal Process. Mag..

[3]  José Carlos Príncipe,et al.  A Reproducing Kernel Hilbert Space Framework for Information-Theoretic Learning , 2008, IEEE Transactions on Signal Processing.

[4]  L. Rudin,et al.  Feature-oriented image enhancement using shock filters , 1990 .

[5]  Dalong Li,et al.  Support vector regression based image denoising , 2009, Image Vis. Comput..

[6]  Sergios Theodoridis,et al.  A geometric approach to Support Vector Machine (SVM) classification , 2006, IEEE Transactions on Neural Networks.

[7]  Erkan Besdok,et al.  Impulsive Noise Suppression from Images by Using Anfis Interpolant and Lillietest , 2004, EURASIP J. Adv. Signal Process..

[8]  N. Aronszajn Theory of Reproducing Kernels. , 1950 .

[9]  Shie Mannor,et al.  The kernel recursive least-squares algorithm , 2004, IEEE Transactions on Signal Processing.

[10]  Sergios Theodoridis,et al.  Sliding Window Generalized Kernel Affine Projection Algorithm Using Projection Mappings , 2008, EURASIP J. Adv. Signal Process..

[11]  Mila Nikolova,et al.  Semi-explicit Solution and Fast Minimization Scheme for an Energy with ℓ1-Fitting and Tikhonov-Like Regularization , 2009, Journal of Mathematical Imaging and Vision.

[12]  Eero P. Simoncelli,et al.  A Machine Learning Framework for Adaptive Combination of Signal Denoising Methods , 2007, 2007 IEEE International Conference on Image Processing.

[13]  Raymond H. Chan,et al.  Minimization of a Detail-Preserving Regularization Functional for Impulse Noise Removal , 2007, Journal of Mathematical Imaging and Vision.

[14]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[15]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[16]  P. Mikusinski,et al.  Introduction to Hilbert spaces with applications , 1990 .

[17]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[18]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[19]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[20]  Edward R. Vrscay,et al.  Fractal image denoising , 2003, IEEE Trans. Image Process..

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

[22]  G. Wahba,et al.  Some results on Tchebycheffian spline functions , 1971 .

[23]  Seongjai Kim,et al.  PDE-based image restoration: a hybrid model and color image denoising , 2006, IEEE Transactions on Image Processing.

[24]  Aleksandra Pizurica,et al.  Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising , 2006, IEEE Transactions on Image Processing.

[25]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[26]  S. Theodoridis,et al.  Reduced Convex Hulls: A Geometric Approach to Support Vector Machines [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[27]  James Mercer Sturm-Liouville Series of Normal Functions in the Theory of Integral Equations , 1912 .

[28]  Michael L. Lightstone,et al.  A new efficient approach for the removal of impulse noise from highly corrupted images , 1996, IEEE Trans. Image Process..

[29]  O. Nelles,et al.  An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.

[30]  Stanley Osher,et al.  Total variation based image restoration with free local constraints , 1994, Proceedings of 1st International Conference on Image Processing.

[31]  L. A. Li︠u︡sternik,et al.  Elements of Functional Analysis , 1962 .

[32]  J. Mercer Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations , 1909 .

[33]  Charles K. Chui,et al.  A universal noise removal algorithm with an impulse detector , 2005, IEEE Transactions on Image Processing.

[34]  Alan L. Yuille,et al.  A mathematical analysis of the motion coherence theory , 1989, International Journal of Computer Vision.

[35]  I. Selesnick,et al.  Bivariate shrinkage with local variance estimation , 2002, IEEE Signal Processing Letters.

[36]  Jacques Froment,et al.  Reconstruction of Wavelet Coefficients Using Total Variation Minimization , 2002, SIAM J. Sci. Comput..

[37]  Bernhard Schölkopf,et al.  Iterative kernel principal component analysis for image modeling , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Ying Chen,et al.  Image Denoising Based on Wavelet Support Vector Machine , 2006, 2006 International Conference on Computational Intelligence and Security.

[39]  Charles Kervrann,et al.  Local Adaptivity to Variable Smoothness for Exemplar-Based Image Regularization and Representation , 2008, International Journal of Computer Vision.

[40]  R. Suoranta,et al.  Robust median filter with adaptive window length , 1991, 1991., IEEE International Sympoisum on Circuits and Systems.