Subject Areas : journal of Artificial Intelligence in Electrical Engineering
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Keywords:
Abstract :
References:
[1] D. L. Donoho, “Compressed sensing,” IEEE Trans. on Information Theory, vol. 52, pp. 1289–1306, April 2006.
[2] L. Gan, “Block Compressed Sensing of Natural Images ,” in Proc. of the 15th Intl. Conf. on Digital Signal Processing (DSP 2007), 2007.
[3] S. Mun and J. E. Fowler, “Block Compressed Sensing of Images Using Directional Transforms,” 16th IEEE Intl. Conf. on Image Processing (ICIP 2009), 2009.
[4] J. Haupt and R. Nowak, “Signal reconstruction from noisy random projections,” IEEE Trans. on Information Theory, vol. 52, no. 9, pp. 4036–4048, September 2006.
[5] J. Zhang, D. Zhao and et al., “Image Compressive Sensing Recovery via Collaborative Sparsity,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 3, Sep. 2012.
[6] J. Zhang, D. Zhao and et al., “Image Restoration Using Joint Statistical Modeling in a Space Transform Domain,” IEEE Trans. on Circuits and systems for Video Technology, vol. 24, No. 6, 2014.
[7] H. J. Trussell and M. R. Civanlar, “The Landweber Iteration and Projection onto Convex Sets,” IEEE Trans. on Acoustics, Speech and Signal Processing, vol. Assp-33, no. 6, 1985.
[8] I. Daubechies, M. Defrise, and C. De-Mol, “An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint,” Pure Appl. Math., vol. 57, no. 11, pp. 1413-1457, Nov. 2004.
[9] J. J. Fuchs, “On Sparse Representations in Arbitrary Redandants Bases,” IEEE Trans. on Information Theory, vol. 50, no. 6, June 2004.
[10] M. Elad, “Sparse and Redundant Representations,” springer, 2010.
[11] L. E. Frank and J. H. Friedman, “A statistical view of some chemometrics regression tools,” Technometrics, vol. 35, no. 2, pp. 109–135, 1993.
[12] J. H. Friedman, “Fast sparse regression and classification,” Int. J. Forecasting, vol. 28, no. 3, pp. 722–738, Jul./Sep. 2012.
[13] C. Gao, N. Wang, Q. Yu, and Z. Zhang, “A feasible nonconvex relaxation approach to feature selection,” in Proc. 25th AAAI Conf. Artif. Intell., pp. 356–361, 2011.
[14] K. Hayashi, M. Nagahara and T. Tanaka, “A User's Guide to Compressed Sensing for Communication Systems,” IEEE Trans. on commun., no. 3, March 2013.
[15] J. Bioucas-Dias and M. Figueiredo, “A new TwIST: Two step iterative shrinkage/ thresholding algorithms for image restoration,” IEEE Trans. on Image Processing, vol.16, no. 12, pp. 2992-3004, Dec. 2007.
[16] A. Beck and M. Tebulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci., vol. 2, no. 1, pp. 183-202, Jan. 2009.
[17] R. Chartrand and W. Yin, “Iteratively reweighted algorithms for compressive sensing, ” in Proc. ICASSP, Las Vegas, USA, pp. 3869-3872, Mar.-Apr. 2008.
[18] S. Boyd and et.al., “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, 2010.
[19] T. Goldstein and S. Osher, “The split Bregman method for ℓ1 regularized problems,” SIAM J. Imag. Sci., vol. 2, no. 2, pp. 323-343, Apr. 2009.
[20] P. L. Combettes and J.C. Pesquet, “Proximal splitting methods in signal processing,” in Fixed-Point Algorithms for Inverse Problems in Science and Engineering. Berlin, Germany: Springer-Verlag, pp. 185–212, 2011.
[21] S. Li and H. Qi, “A Douglas-Rachford Splitting Approach to Compressed Sensing Image Recovery Using Low Rank Regularization,” IEEE Trans. on image processing, vol. 24, No. 11, 2015.
[1] D. L. Donoho, “Compressed sensing,” IEEE Trans. on Information Theory, vol. 52, pp. 1289–1306, April 2006.
[2] L. Gan, “Block Compressed Sensing of Natural Images ,” in Proc. of the 15th Intl. Conf. on Digital Signal Processing (DSP 2007), 2007.
[3] S. Mun and J. E. Fowler, “Block Compressed Sensing of Images Using Directional Transforms,” 16th IEEE Intl. Conf. on Image Processing (ICIP 2009), 2009.
[4] J. Haupt and R. Nowak, “Signal reconstruction from noisy random projections,” IEEE Trans. on Information Theory, vol. 52, no. 9, pp. 4036–4048, September 2006.
[5] J. Zhang, D. Zhao and et al., “Image Compressive Sensing Recovery via Collaborative Sparsity,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 2, no. 3, Sep. 2012.
[6] J. Zhang, D. Zhao and et al., “Image Restoration Using Joint Statistical Modeling in a Space Transform Domain,” IEEE Trans. on Circuits and systems for Video Technology, vol. 24, No. 6, 2014.
[7] H. J. Trussell and M. R. Civanlar, “The Landweber Iteration and Projection onto Convex Sets,” IEEE Trans. on Acoustics, Speech and Signal Processing, vol. Assp-33, no. 6, 1985.
[8] I. Daubechies, M. Defrise, and C. De-Mol, “An Iterative Thresholding Algorithm for Linear Inverse Problems with a Sparsity Constraint,” Pure Appl. Math., vol. 57, no. 11, pp. 1413-1457, Nov. 2004.
[9] J. J. Fuchs, “On Sparse Representations in Arbitrary Redandants Bases,” IEEE Trans. on Information Theory, vol. 50, no. 6, June 2004.
[10] M. Elad, “Sparse and Redundant Representations,” springer, 2010.
[11] L. E. Frank and J. H. Friedman, “A statistical view of some chemometrics regression tools,” Technometrics, vol. 35, no. 2, pp. 109–135, 1993.
[12] J. H. Friedman, “Fast sparse regression and classification,” Int. J. Forecasting, vol. 28, no. 3, pp. 722–738, Jul./Sep. 2012.
[13] C. Gao, N. Wang, Q. Yu, and Z. Zhang, “A feasible nonconvex relaxation approach to feature selection,” in Proc. 25th AAAI Conf. Artif. Intell., pp. 356–361, 2011.
[14] K. Hayashi, M. Nagahara and T. Tanaka, “A User's Guide to Compressed Sensing for Communication Systems,” IEEE Trans. on commun., no. 3, March 2013.
[15] J. Bioucas-Dias and M. Figueiredo, “A new TwIST: Two step iterative shrinkage/ thresholding algorithms for image restoration,” IEEE Trans. on Image Processing, vol.16, no. 12, pp. 2992-3004, Dec. 2007.
[16] A. Beck and M. Tebulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci., vol. 2, no. 1, pp. 183-202, Jan. 2009.
[17] R. Chartrand and W. Yin, “Iteratively reweighted algorithms for compressive sensing, ” in Proc. ICASSP, Las Vegas, USA, pp. 3869-3872, Mar.-Apr. 2008.
[18] S. Boyd and et.al., “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, 2010.
[19] T. Goldstein and S. Osher, “The split Bregman method for ℓ1 regularized problems,” SIAM J. Imag. Sci., vol. 2, no. 2, pp. 323-343, Apr. 2009.
[20] P. L. Combettes and J.C. Pesquet, “Proximal splitting methods in signal processing,” in Fixed-Point Algorithms for Inverse Problems in Science and Engineering. Berlin, Germany: Springer-Verlag, pp. 185–212, 2011.
[21] S. Li and H. Qi, “A Douglas-Rachford Splitting Approach to Compressed Sensing Image Recovery Using Low Rank Regularization,” IEEE Trans. on image processing, vol. 24, No. 11, 2015.
