On the Role of Sparse and Redundant Representations in Image Processing
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[1] Miss A.O. Penney. (b) , 1974, The New Yale Book of Quotations.
[2] B. R. Hunt,et al. Digital Image Restoration , 1977 .
[3] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Andrew Blake,et al. Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.
[5] Martin Vetterli,et al. A theory of multirate filter banks , 1987, IEEE Trans. Acoust. Speech Signal Process..
[6] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[7] Edward H. Adelson,et al. Shiftable multiscale transforms , 1992, IEEE Trans. Inf. Theory.
[8] I. Johnstone,et al. Ideal spatial adaptation by wavelet shrinkage , 1994 .
[9] D. Donoho,et al. Translation-Invariant De-Noising , 1995 .
[10] David L. Donoho,et al. De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.
[11] C. Burrus,et al. Noise reduction using an undecimated discrete wavelet transform , 1996, IEEE Signal Processing Letters.
[12] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[13] Edward H. Adelson,et al. Noise removal via Bayesian wavelet coring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.
[14] Axthonv G. Oettinger,et al. IEEE Transactions on Information Theory , 1998 .
[15] B. Vidakovic. Nonlinear wavelet shrinkage with Bayes rules and Bayes factors , 1998 .
[16] S. Mallat. A wavelet tour of signal processing , 1998 .
[17] Pierre Moulin,et al. Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian and Complexity Priors , 1999, IEEE Trans. Inf. Theory.
[18] Hyvarinen. Sparse code shrinkage: denoising of nongaussian data by maximum likelihood estimation , 1999, Neural computation.
[19] Kannan Ramchandran,et al. Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.
[20] Alexei A. Efros,et al. Texture synthesis by non-parametric sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[21] Aapo Hyvärinen,et al. Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation , 1999, Neural Computation.
[22] D. Donoho,et al. Atomic Decomposition by Basis Pursuit , 2001 .
[23] R. Nowak,et al. Fast wavelet-based image deconvolution using the EM algorithm , 2001, Conference Record of Thirty-Fifth Asilomar Conference on Signals, Systems and Computers (Cat.No.01CH37256).
[24] Robert D. Nowak,et al. Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior , 2001, IEEE Trans. Image Process..
[25] Mário A. T. Figueiredo,et al. Wavelet-Based Image Estimation : An Empirical Bayes Approach Using Jeffreys ’ Noninformative Prior , 2001 .
[26] William T. Freeman,et al. Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.
[27] Robert D. Nowak,et al. An EM algorithm for wavelet-based image restoration , 2003, IEEE Trans. Image Process..
[28] I. Daubechies,et al. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.
[29] E. Candès,et al. Astronomical image representation by the curvelet transform , 2003, Astronomy & Astrophysics.
[30] Fionn Murtagh,et al. Fast communication , 2002 .
[31] Onur G. Guleryuz. Nonlinear approximation based image recovery using adaptive sparse reconstructions , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[32] Antonin Chambolle,et al. A l1-Unified Variational Framework for Image Restoration , 2004, ECCV.
[33] Andy M. Yip,et al. Recent Developments in Total Variation Image Restoration , 2004 .
[34] E. Candès,et al. New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities , 2004 .
[35] G.. A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .
[36] Nikos Paragios,et al. Handbook of Mathematical Models in Computer Vision , 2005 .
[37] Patrick L. Combettes,et al. Signal Recovery by Proximal Forward-Backward Splitting , 2005, Multiscale Model. Simul..
[38] Robert D. Nowak,et al. A bound optimization approach to wavelet-based image deconvolution , 2005, IEEE International Conference on Image Processing 2005.
[39] Jean-Michel Morel,et al. A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[40] D. Donoho,et al. Neighborliness of randomly projected simplices in high dimensions. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[41] Kun Huang,et al. A multiscale hybrid linear model for lossy image representation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[42] D. Donoho,et al. Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) , 2005 .
[43] A. Bruckstein,et al. K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .
[44] S. Shankar Sastry,et al. Generalized principal component analysis (GPCA) , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[45] Michael Elad,et al. Image Denoising with Shrinkage and Redundant Representations , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[46] Michael Elad,et al. Why Simple Shrinkage Is Still Relevant for Redundant Representations? , 2006, IEEE Transactions on Information Theory.
[47] M. Elad,et al. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.
[48] Onur G. Guleryuz,et al. Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising-part I: theory , 2006, IEEE Transactions on Image Processing.
[49] Kun Huang,et al. Multiscale Hybrid Linear Models for Lossy Image Representation , 2006, IEEE Transactions on Image Processing.
[50] Michael Elad,et al. Analysis versus synthesis in signal priors , 2006, 2006 14th European Signal Processing Conference.
[51] David Mumford,et al. Empirical Statistics and Stochastic Models for Visual Signals , 2006 .
[52] Michael Elad,et al. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.
[53] José M. Bioucas-Dias,et al. A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.
[54] Mei Han,et al. Soft Edge Smoothness Prior for Alpha Channel Super Resolution , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[55] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[56] Michael Elad,et al. Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization , 2007 .
[57] John Wright,et al. Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[58] Thomas S. Huang,et al. Face hallucination VIA sparse coding , 2008, 2008 15th IEEE International Conference on Image Processing.
[59] Thomas S. Huang,et al. Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[60] Allen Y. Yang,et al. Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data , 2008, SIAM Rev..
[61] Michael Elad,et al. Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.
[62] E.J. Candes,et al. An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[63] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[64] Stephen J. Wright,et al. Sparse reconstruction by separable approximation , 2009, IEEE Trans. Signal Process..
[65] Michael Elad,et al. From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..
[66] Stephen J. Wright,et al. Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.
[67] Gabriel Peyré,et al. Manifold models for signals and images , 2009, Comput. Vis. Image Underst..