Computing Sparse Representations of Multidimensional Signals Using Kronecker Bases
暂无分享,去创建一个
[1] C. Loan,et al. Approximation with Kronecker Products , 1992 .
[2] Zhifeng Zhang,et al. Adaptive time-frequency decompositions , 1994 .
[3] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[4] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[5] Joos Vandewalle,et al. A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..
[6] Joos Vandewalle,et al. On the Best Rank-1 and Rank-(R1 , R2, ... , RN) Approximation of Higher-Order Tensors , 2000, SIAM J. Matrix Anal. Appl..
[7] C. Loan. The ubiquitous Kronecker product , 2000 .
[8] Michael Elad,et al. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[9] Yuanqing Li,et al. Sparse Representation and Its Applications in Blind Source Separation , 2003, NIPS.
[10] Rémi Gribonval,et al. Sparse representations in unions of bases , 2003, IEEE Trans. Inf. Theory.
[11] Judith M. Ford,et al. Combining Kronecker Product Approximation with Discrete Wavelet Transforms to Solve Dense, Function-Related Linear Systems , 2003, SIAM J. Sci. Comput..
[12] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[13] E. Tyrtyshnikov. Kronecker-product approximations for some function-related matrices , 2004 .
[14] Kinjiro Amano,et al. Information limits on neural identification of colored surfaces in natural scenes , 2004, Visual Neuroscience.
[15] D. Donoho,et al. Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) , 2005 .
[16] Misha Elena Kilmer,et al. Kronecker product approximation for preconditioning in three-dimensional imaging applications , 2006, IEEE Transactions on Image Processing.
[17] Emmanuel J. Candès,et al. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.
[18] Tamara G. Kolda,et al. MATLAB Tensor Toolbox , 2006 .
[19] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[20] Michael Elad,et al. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.
[21] Mário A. T. Figueiredo,et al. Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.
[22] E. Candès,et al. Sparsity and incoherence in compressive sampling , 2006, math/0611957.
[23] Mohamed-Jalal Fadili,et al. Sparsity and Morphological Diversity in Blind Source Separation , 2007, IEEE Transactions on Image Processing.
[24] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[25] J. Tropp. On the Linear Independence of Spikes and Sines , 2007, 0709.0517.
[26] Michael Elad,et al. Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .
[27] Eugene E. Tyrtyshnikov,et al. Tucker Dimensionality Reduction of Three-Dimensional Arrays in Linear Time , 2008, SIAM J. Matrix Anal. Appl..
[28] Minh N. Do,et al. A Theory for Sampling Signals from a Union of Subspaces , 2022 .
[29] Ting Sun,et al. Single-pixel imaging via compressive sampling , 2008, IEEE Signal Process. Mag..
[30] E.J. Candes,et al. An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.
[31] Deanna Needell,et al. Greedy signal recovery review , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.
[32] V. Mehrmann,et al. Sparse solutions to underdetermined Kronecker product systems , 2009 .
[33] Y. Rivenson,et al. Practical compressive sensing of large images , 2009, 2009 16th International Conference on Digital Signal Processing.
[34] Tamara G. Kolda,et al. Tensor Decompositions and Applications , 2009, SIAM Rev..
[35] Andrzej Cichocki,et al. Estimation of Sparse Nonnegative Sources from Noisy Overcomplete Mixtures Using MAP , 2009, Neural Computation.
[36] Adrian Stern,et al. Compressed Imaging With a Separable Sensing Operator , 2009, IEEE Signal Processing Letters.
[37] Andrzej Cichocki,et al. Nonnegative Matrix and Tensor Factorization T , 2007 .
[38] Stephen J. Wright,et al. Computational Methods for Sparse Solution of Linear Inverse Problems , 2010, Proceedings of the IEEE.
[39] Michael Elad,et al. On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.
[40] Yonina C. Eldar,et al. Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.
[41] A. Cichocki,et al. Generalizing the column–row matrix decomposition to multi-way arrays , 2010 .
[42] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[43] Ivan Oseledets,et al. Tensor-Train Decomposition , 2011, SIAM J. Sci. Comput..
[44] Andrzej Cichocki,et al. Block sparse representations of tensors using Kronecker bases , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[45] Richard G. Baraniuk,et al. Kronecker Compressive Sensing , 2012, IEEE Transactions on Image Processing.
[46] Andrzej Cichocki,et al. Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective , 2014, IEEE Signal Processing Magazine.
[47] Emanuel A. P. Habets,et al. IEEE Journal of Selected Topics in Signal Processing , 2019, IEEE Signal Processing Magazine.