Identifiability and Stability in Blind Deconvolution under Minimal Assumptions

D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Process. Mag. , vol. 13, no. 3, pp. 43–64, May 1996

1996

Earlier work this paper cites.

T. Chan and C.-K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process. , vol. 7, no. 3, pp. 370–375, Mar 1998

1998

Earlier work this paper cites.

G. Harikumar and Y. Bresler, “FIR perfect signal reconstruction from multiple convolutions: minimum deconvolver orders,” IEEE Trans. Signal Process. , vol. 46, no. 1, pp. 215–218, Jan 1998

1998

Earlier work this paper cites.

L. Litwin, Jr., “Blind channel equalization,” IEEE Potentials , vol. 18, no. 4, pp. 9–12, Oct 1999

1999

Earlier work this paper cites.

O. Yilmaz, Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data . Society of Exploration Geophysicists, 2001

2001

Earlier work this paper cites.

D. L. Donoho and M. Elad, “Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 \ell_{1} minimization,” Proc. Natl. Acad. Sci. , vol. 100, no. 5, pp. 2197–2202, 2003

Original

2003

Earlier work this paper cites.

K. Herrity, R. Raich, and A. O. Hero III, “Blind reconstruction of sparse images with unknown point spread function,” Proc. SPIE , vol. 6814, pp. 68 140K.1–68 140K.11, 2008

2008

Earlier work this paper cites.

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

2009

Earlier work this paper cites.

P. A. Naylor and N. D. Gaubitch, Speech Dereverberation , 1st ed. Springer, 2010

2010

Cited alongside, same era.

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

2011

Cited alongside, same era.

Y. Eldar, D. Needell, and Y. Plan, “Uniqueness conditions for low-rank matrix recovery,” Appl. and Computational Harmonic Anal. , vol. 33, no. 2, pp. 309 – 314, 2012

2012

Cited alongside, same era.

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

2013

Cited alongside, same era.

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

S. Ling and T. Strohmer, “Self-calibration and biconvex compressive sensing,” arXiv preprint arXiv:1501.06864 , 2015

Original

2015

Closest in time.

K. Lee, Y. Li, M. Junge, and Y. Bresler, “Blind recovery of sparse signals from subsampled convolution,” arXiv preprint arXiv:1511.06149 , 2015

Original

2015

Closest in time.

——, “Stability in blind deconvolution of sparse signals and reconstruction by alternating minimization,” Int. Conf. Sampling Theory and Applications (SampTA) , 2015

2015

Closest in time.

Y. Chi, “Guaranteed blind sparse spikes deconvolution via lifting and convex optimization,” arXiv preprint arXiv:1506.02751 , 2015

Original

2015

Closest in time.

Y. Li, K. Lee, and Y. Bresler, “Identifiability in blind deconvolution with subspace or sparsity constraints,” arXiv preprint arXiv:1505.03399 , 2015