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Blind deconvolution (BD), the resolution of a signal and a filter given their convolution, arises in many applications.
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M. Salman Asif, W. Mantzel, and J. Romberg, “Random channel coding and blind deconvolution,” in Proc. 47th Annu. Allerton Conf. Commun., Control, and Computing , Sept 2009, pp. 1021–1025
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D. Krishnan, T. Tay, and R. Fergus, “Blind deconvolution using a normalized sparsity measure,” in Proc. Conf. Comput. Vision and Pattern Recognition (CVPR) . IEEE, June 2011, pp. 233–240
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——, “Sparse blind deconvolution: What cannot be done,” in Proc. Int. Symp. Inform. Theory (ISIT) . IEEE, June 2014, pp. 3002–3006
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Y. Li, K. Lee, and Y. Bresler, “Identifiability of blind deconvolution with subspace or sparsity constraints,” in Signal Process. with Adaptive Sparse Structured Representations (SPARS) , 2015
2015
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A. Repetti, M. Pham, L. Duval, E. Chouzenoux, and J.-C. Pesquet, “Euclid in a taxicab: Sparse blind deconvolution with smoothed ℓ 1 \ell_{1} / ℓ 2 \ell_{2} regularization,” IEEE Signal Process. Lett. , vol. 22, no. 5, pp. 539–543, May 2015
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E. J. Candès, T. Strohmer, and V. Voroninski, “Phaselift: Exact and stable signal recovery from magnitude measurements via convex programming,” Commun. Pure Appl. Math. , vol. 66, no. 8, pp. 1241–1274, 2013
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A. Ahmed, B. Recht, and J. Romberg, “Blind deconvolution using convex programming,” IEEE Trans. Inf. Theory , vol. 60, no. 3, pp. 1711–1732, Mar 2014
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2015
Closest in time.
K. Lee, Y. Li, M. Junge, and Y. Bresler, “Stability in blind deconvolution of sparse signals and reconstruction by alternating minimization,” Int. Conf. Sampling Theory and Applications (SampTA) , 2015
2015
Closest in time.