Publications about 'sparse matrices;'

Articles in journal or book chapters

  1. R.G. Baraniuk, E. Candes, M. Elad, and Yi Ma. Applications of Sparse Representation and Compressive Sensing [Scanning the Issue]. Proceedings of the IEEE, 98(6):906-909, june 2010. Keyword(s): NP-hard, compressive sensing, convex optimization, greedy methods, high-dimensional data, linear combination, overcomplete dictionary, sparse representation, computational complexity, signal processing, sparse matrices;. [Abstract] [bibtex-entry]

  2. E.J. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2):489-509, feb. 2006. Keyword(s): Fourier coefficient, convex optimization, discrete-time signal, image reconstruction, incomplete frequency information, linear programming, minimization problem, nonlinear sampling theorem, piecewise constant object, probability value, robust uncertainty principle, signal reconstruction, sparse random matrix, trigonometric expansion, Fourier analysis, convex programming, image reconstruction, image sampling, indeterminancy, linear programming, minimisation, piecewise constant techniques, probability, signal reconstruction, signal sampling, sparse matrices;. [Abstract] [bibtex-entry]

  3. E.J. Candes and T. Tao. Decoding by linear programming. IEEE Transactions on Information Theory, 51(12):4203-4215, December 2005. Keyword(s): Gaussian random matrix, basis pursuit, linear code decoding, linear programming, minimization problem, natural error correcting problem, simple convex optimization problem, sparse solution, uncertainty principle, Gaussian processes, convex programming, decoding, error correction codes, indeterminancy, linear codes, linear programming, minimisation, random codes, sparse matrices;. [Abstract] [bibtex-entry]

Conference articles

  1. Thomas M. Benson, Daniel P. Campbell, and Daniel A. Cook. Gigapixel spotlight synthetic aperture radar backprojection using clusters of GPUs and CUDA. In 2012 IEEE Radar Conference, pages 0853-0858, May 2012. Keyword(s): SAR Processing, Back-Projection, Time-Domain Back-Projection, TDBP, CUDA, GPU, SAR Focusing, Azimuth Focusing, fast Fourier transforms, graphics processing units, parallel architectures, radar computing, radar imaging, resource allocation, synthetic aperture radar, CUDA Clusters, GPU Clusters, SAR image formation, computing nodes, fast Fourier transforms, gigapixel scale data set, gigapixel spotlight synthetic aperture radar backprojection, graphics processing units, image formation algorithms, image formation framework, nonplanar surfaces, wavefront planarity, Graphics processing unit, History, Interpolation, Kernel, Scalability, Sparse matrices, Synthetic aperture radar. [Abstract] [bibtex-entry]

  2. E. Candes, N. Braun, and M. Wakin. SPARSE SIGNAL AND IMAGE RECOVERY FROM COMPRESSIVE SAMPLES. In Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on, pages 976-979, april 2007. Keyword(s): compressive sampling, data acquisition, image recovery, magnetic resonance imaging, medical imaging, model-based framework, random measurements, random noise-like basis, signal reconstruction, sparse signal recovery, biomedical MRI, biomedical measurement, data acquisition, image coding, image reconstruction, image sampling, medical image processing, random noise, sparse matrices;. [Abstract] [bibtex-entry]



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This collection of SAR literature is far from being complete.
It is rather a collection of papers which I store in my literature data base. Hence, the list of publications under PUBLICATIONS OF AUTHOR'S NAME should NOT be mistaken for a complete bibliography of that author.

Last modified: Mon Feb 1 16:41:15 2021
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .

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