Publications of Emmanuel J. Candès

Articles in journal or book chapters

1. E.J. Candes and Y. Plan. A Probabilistic and RIPless Theory of Compressed Sensing. IEEE Transactions on Information Theory, 57(11):7235-7254, November 2011. Keyword(s): Fourier coefficients, Gaussian model, RIPless theory, compressed sensing, frequency measurements, probabilistic theory, probability distribution, restricted isometry property, signal random model, sparse signals, Fourier analysis, data compression, random processes, signal reconstruction, statistical distributions;. [Abstract] [bibtex-entry]



2. Emmanuel J. Candès, Xiaodong Li, Yi Ma, and John Wright. Robust Principal Component Analysis. J. ACM, 58(3), June 2011. Keyword(s): Principal components, sparsity, robustness vis-a-vis outliers, L1-norm minimization, nuclear-norm minimization, duality, low-rank matrices, video surveillance. [Abstract] [bibtex-entry]



3. E.J. Candes and Y. Plan. Matrix Completion With Noise. Proceedings of the IEEE, 98(6):925-936, june 2010. Keyword(s): compressed sensing, convex optimization problem, data constraints, low rank matrices, matrix completion, nuclear norm minimization, data integrity, matrix algebra, minimisation, noise, signal sampling;. [Abstract] [bibtex-entry]



4. E.J. Candes and T. Tao. The Power of Convex Relaxation: Near-Optimal Matrix Completion. IEEE Transactions on Information Theory, 56(5):2053-2080, May 2010. Keyword(s): collaborative filtering, convex relaxation, free probability, information theoretic limit, matrix completion problem, near-optimal matrix completion, nuclear norm minimization, random matrices, random matrix theory, semidefinite programming, convex programming, information theory, random processes;. [Abstract] [bibtex-entry]



5. Emmanuel J. Candes. The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9-10):589-592, 2008. Keyword(s): Compressive Sensing, Compressed Sensing. [bibtex-entry]



6. E.J. Candes and M.B. Wakin. An Introduction To Compressive Sampling. IEEE Signal Processing Magazine, 25(2):21-30, March 2008. Keyword(s): Relatively few wavelet, compressed sensing, compressive sampling, data acquisition, image recovery, sampling paradigm, sensing paradigm, signal recovery, data acquisition, image processing, signal processing equipment, signal sampling;. [Abstract] [bibtex-entry]



7. 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]



8. Emmanuel J. Candes, Justin K. Romberg, and Terence Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8):1207-1223, 2006. Keyword(s): Compressive Sensing, Compressed Sensing. [Abstract] [bibtex-entry]



9. 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]



10. Jean-Luc Starck, E.J. Candes, and D.L. Donoho. The curvelet transform for image denoising. IEEE Transactions on Image Processing, 11(6):670-684, June 2002. Keyword(s): Cartesian samples, Fourier space, Fourier-domain, approximate digital Radon transform, approximate digital implementations, concentric squares geometry, curvelet coefficients, curvelet transform, decimated wavelet transforms, exact reconstruction, filter bank, frequency domain, image denoising, interpolation, low computational complexity, overcomplete wavelet pyramid, pseudo-polar sampling set, rectopolar grid, ridgelet transform, stability, tree-based Bayesian posterior mean methods, trous wavelet filters, undecimated wavelet transforms, visual performance, wavelet-based image reconstruction, white noise, Fourier transforms, Radon transforms, channel bank filters, filtering theory, image reconstruction, interpolation, wavelet transforms, white noise;. [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.

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