Compressive sensing reconstruction with prior information by iteratively reweighted least-squares

IEEE Transactions on Signal Processing

Volumn 57, Issue 6, 2009, Pages 2424-2431

  Miosso, Cristiano Jacques ^a   von Borries, Ricardo ^a   Argaez M ^b   Velazquez, L ^b   Quintero, C ^b   Potes, C M ^a  

^a University of Texas at El Paso   (United States)

^b UNIVERSITY OF TEXAS AT EL PASO   (United States)

Author keywords

Compressive sensing; Iteratively reweighted least squares; Prior information; Sensor networks; Sparsity

Indexed keywords

COMPRESSIVE SENSING; COMPUTATION TIME; ITERATIVELY REWEIGHTED LEAST-SQUARES; LINEAR MEASUREMENTS; NON-ZERO COEFFICIENTS; NUMBER OF ITERATIONS; OPTIMIZATION PROBLEMS; PRIOR INFORMATION; PRIOR KNOWLEDGE; RECONSTRUCTION METHOD; SIMULATION RESULT; SPARSE SIGNALS; SPARSITY; UNAMBIGUOUS RECONSTRUCTION; WEIGHTING SCHEME;

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL FLUID DYNAMICS; RESTORATION; SIGNAL RECONSTRUCTION;

SENSOR NETWORKS;

EID: 66849141873     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2009.2016889     Document Type: Article

References (14)

1. R. von Borries, C. J. Miosso, and C. M. Potes, "Compressed sensing using prior information," in Proc. 2nd IEEE Int. Workshop Computational Advances Multi-Sensor Adaptive Processing (CAMPSAP), Dec. 12-14, 2007, pp. 121-124.
2. R. von Borries and C. J. Miosso, "Compressed sensing with prior information on the support region of the sparse domain," 2008, in preparation.
3. R. Chartrand and W. Yin, "Iteratively reweighted algorithms for compressive sensing," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), Las Vegas, NV, Apr. 4, 2008, pp. 3869-3872.
4. R. G. Baraniuk, "Compressive sensing lecture notes," IEEE Signal Process. Mag., vol. 24, no. 4, pp. 118-121, 2007.
5. E. J. Candès and M. B. Wakin, "An introduction to compressive sampling a sensing/sampling paradigm that goes against the common knowledge in data acquisition," IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21-30, 2008.
6. M. F. Duarte, M. B. Wakin, and R. G. Baraniuk, "Fast reconstruction of piecewise smooth signals from incoherent projections," presented at the Structure et Parcimonie Pour la Représentation Adaptative de Signaux (SPARS), Rennes, France, Nov. 16-18, 2005.
7. C. La and M. N. Do, "Signal reconstruction using sparse tree representations," presented at the SPIE Conf. Wavelet Applications Signal Image Processing XI, San Diego, CA, Jul. 2005.
8. J. Garcia-Frias and I. Esnaola, "Exploiting prior knowledge in the recovery of signals from noisy random projections," in Data Compression Conf. (DCC), 2007, pp. 333-342.
9. M. Elad and M. Aharon, "Image denoising via sparse and redundant representations over learned dictionaries," IEEE Trans. Image Process., vol. 15, pp. 3736-3745, 2006.
10. M. Elad and M. Aharon, "Image denoising via learned dictionaries and sparse representation," in IEEE Computer Soc. Conf. Computer Vision Pattern Recognition, 2006, vol. 1, pp. 895-900.
11. E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information," IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489-509, Feb. 2006.
12. E. J. Candès and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?," IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
13. B. D. Rao and K. Kreutz-Delgado, "An affine scaling methodology for best basis selection," IEEE Trans. Signal Process., vol. 47, no. 1, pp. 187-200, Jan. 1999.
14. C. D. Meyer, Matrix Analysis and Applied Linear Algebra. Philadelphia, PA: SIAM, 2000.