TY - JOUR AU - Donoho, D. L. PY - 2006 DA - 2006// TI - Compressed sensing JO - IEEE Trans. Inform. Theory VL - 52 UR - https://doi.org/10.1109/TIT.2006.871582 DO - 10.1109/TIT.2006.871582 ID - Donoho2006 ER - TY - STD TI - AK Fletcher, S Rangan, VK Goyal, K Ramchandran, Denoising by sparse approximation: error bounds based on rate-distortion theory. EURASIP J. Adv. Sig. Process. 2006(1), 026318 (2006). https://doi.org/10.1155/ASP/2006/26318. ID - ref2 ER - TY - BOOK AU - Davenport, M. A. AU - Duarte, M. F. AU - Eldar, Y. C. AU - Kutyniok, G. PY - 2012 DA - 2012// TI - Introduction to Compressed Sensing PB - Cambridge University Press CY - Cambridge UR - https://doi.org/10.1017/CBO9780511794308.002 DO - 10.1017/CBO9780511794308.002 ID - Davenport2012 ER - TY - JOUR AU - Candès, E. J. AU - Romberg, J. K. AU - Tao, T. PY - 2006 DA - 2006// TI - Stable signal recovery from incomplete and inaccurate measurements JO - Commun. Pur. Appl. Math. VL - 59 UR - https://doi.org/10.1002/cpa.20124 DO - 10.1002/cpa.20124 ID - Candès2006 ER - TY - JOUR AU - Tibshirani, R. PY - 1994 DA - 1994// TI - Regression shrinkage and selection via the lasso JO - J. R. Stat. Soc. Ser. B VL - 58 ID - Tibshirani1994 ER - TY - JOUR AU - Daubechies, I. AU - Defrise, M. AU - De Mol, C. PY - 2004 DA - 2004// TI - An iterative thresholding algorithm for linear inverse problems with a sparsity constraint JO - Comm. Pure Appl. Math. VL - 57 UR - https://doi.org/10.1002/cpa.20042 DO - 10.1002/cpa.20042 ID - Daubechies2004 ER - TY - STD TI - ET Hale, W Yin, Y Zhang, A fixed-point continuation method for l1-regularized minimization with applications to compressed sensing. Technical report, Rice University (2007). ID - ref7 ER - TY - JOUR AU - Wright, S. J. AU - Nowak, R. D. AU - Figueiredo, M. A. T. PY - 2009 DA - 2009// TI - Sparse reconstruction by separable approximation JO - Trans. Sig. Proc. VL - 57 UR - https://doi.org/10.1109/TSP.2009.2016892 DO - 10.1109/TSP.2009.2016892 ID - Wright2009 ER - TY - JOUR AU - Beck, A. AU - Teboulle, M. PY - 2009 DA - 2009// TI - A fast iterative shrinkage-thresholding algorithm for linear inverse problems JO - SIAM J. Img. Sci. VL - 2 UR - https://doi.org/10.1137/080716542 DO - 10.1137/080716542 ID - Beck2009 ER - TY - BOOK AU - Fornasier, M. PY - 2010 DA - 2010// TI - Theoretical Foundations and Numerical Methods for Sparse Recovery PB - Radon Series on Computational and Applied Mathematics CY - Linz UR - https://doi.org/10.1515/9783110226157 DO - 10.1515/9783110226157 ID - Fornasier2010 ER - TY - STD TI - W Guo, W Yin, Edge guided reconstruction for compressive imaging. SIAM J. Imaging Sci. 5(3), 809–834 (2012). https://doi.org/10.1137/110837309. ID - ref11 ER - TY - STD TI - W Yin, S Osher, D Goldfarb, J Darbon, Bregman iterative algorithms for ℓ1-minimization with applications to compressed sensing. SIAM J. Imaging Sci. 1(1), 143–168 (2008). https://doi.org/10.1137/070703983. ID - ref12 ER - TY - STD TI - A Maleki, DL Donoho, Optimally tuned iterative reconstruction algorithms for compressed sensing. IEEE J. Sel. Top. Sig. Process. 4(2), 330–341 (2010). https://doi.org/10.1109/JSTSP.2009.2039176. ID - ref13 ER - TY - STD TI - DL Donoho, A Maleki, A Montanari, in Proc. of 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo). Message passing algorithms for compressed sensing: I. motivation and construction, (2010), pp. 1–5. ID - ref14 ER - TY - STD TI - T Goldstein, C Studer, R Baraniuk, A field guide to forward-backward splitting with a FASTA implementation, 1–24 (2014). arXiv eprint. https://arxiv.org/abs/1411.3406. UR - https://arxiv.org/abs/1411.3406 ID - ref15 ER - TY - BOOK AU - Bishop, C. M. PY - 2006 DA - 2006// TI - Pattern Recognition and Machine Learning (Information Science and Statistics) PB - Springer CY - Secaucus ID - Bishop2006 ER - TY - JOUR AU - Gribonval, R. AU - Cevher, V. AU - Davies, M. E. PY - 2012 DA - 2012// TI - Compressible distributions for high-dimensional statistics JO - IEEE Trans. Inf. Theory VL - 58 UR - https://doi.org/10.1109/TIT.2012.2197174 DO - 10.1109/TIT.2012.2197174 ID - Gribonval2012 ER - TY - STD TI - J Zhang, Y Li, Z Yu, Z Gu, Noisy sparse recovery based on parameterized quadratic programming by thresholding. EURASIP J. Adv. Sig. Proc.2011: (2011). https://doi.org/10.1155/2011/528734. ID - ref18 ER - TY - JOUR AU - Wang, Y. AU - Yin, W. PY - 2010 DA - 2010// TI - Sparse signal reconstruction via iterative support detection JO - SIAM J. Img. Sci. VL - 3 UR - https://doi.org/10.1137/090772447 DO - 10.1137/090772447 ID - Wang2010 ER - TY - STD TI - C Hegde, MF Duarte, V Cevher, in Proceedings of the Workshop on Signal Processing with Adaptive Sparse Representations (SPARS). Compressive Sensing Recovery of Spike Trains Using Structured Sparsity (Saint Malo, 2009). https://infoscience.epfl.ch/record/151471/files/Compressive%20sensing%20recovery%20of%20spike%20trains%20using%20a%20structured%20sparsity%20model.pdf. UR - https://infoscience.epfl.ch/record/151471/files/Compressive%20sensing%20recovery%20of%20spike%20trains%20using%20a%20structured%20sparsity%20model.pdf ID - ref20 ER - TY - JOUR AU - Baraniuk, R. G. AU - Cevher, V. AU - Duarte, M. F. AU - Hegde, C. PY - 2010 DA - 2010// TI - Model-based compressive sensing JO - IEEE Trans. Inf. Theor. VL - 56 UR - https://doi.org/10.1109/TIT.2010.2040894 DO - 10.1109/TIT.2010.2040894 ID - Baraniuk2010 ER - TY - STD TI - R von Borries, CJ Miosso, C Potes, in Computational Advances in Multi-Sensor Adaptive Processing, 2007. CAMPSAP 2007. 2nd IEEE International Workshop On, Compressed sensing using prior information. (2007), pp. 121–124. ID - ref22 ER - TY - STD TI - N Vaswani, W Lu, in Information Theory, 2009. ISIT 2009. IEEE International Symposium On, Modified-CS: Modifying compressive sensing for problems with partially known support. (2009), pp. 488–492. ID - ref23 ER - TY - JOUR AU - Escoda, O. D. AU - Granai, L. AU - Vandergheynst, P. PY - 2006 DA - 2006// TI - On the use of a priori information for sparse signal approximations JO - IEEE Trans. Sig. Process VL - 54 UR - https://doi.org/10.1109/TSP.2006.879306 DO - 10.1109/TSP.2006.879306 ID - Escoda2006 ER - TY - JOUR AU - Khajehnejad, M. A. AU - Xu, W. AU - Avestimehr, A. S. AU - Hassibi, B. PY - 2011 DA - 2011// TI - Analyzing weighted ℓ1 minimization for sparse recovery with nonuniform sparse models JO - IEEE Trans. Sig. Process. VL - 59 UR - https://doi.org/10.1109/TSP.2011.2107904 DO - 10.1109/TSP.2011.2107904 ID - Khajehnejad2011 ER - TY - JOUR AU - Friedlander, M. P. AU - Mansour, H. AU - Saab, R. AU - Yilmaz, O. PY - 2012 DA - 2012// TI - Recovering compressively sampled signals using partial support information JO - IEEE Trans. Inf. Theory VL - 58 UR - https://doi.org/10.1109/TIT.2011.2167214 DO - 10.1109/TIT.2011.2167214 ID - Friedlander2012 ER - TY - JOUR AU - Scarlett, J. AU - Evans, J. S. AU - Dey, S. PY - 2013 DA - 2013// TI - Compressed sensing with prior information: information-theoretic limits and practical decoders JO - IEEE Trans. Sig. Process VL - 61 UR - https://doi.org/10.1109/TSP.2012.2225051 DO - 10.1109/TSP.2012.2225051 ID - Scarlett2013 ER - TY - JOUR AU - Khajehnejad, M. A. AU - Xu, W. AU - Avestimehr, A. S. AU - Hassibi, B. PY - 2011 DA - 2011// TI - Analyzing Weighted ℓ1 Minimization for Sparse Recovery With Nonuniform Sparse Models JO - IEEE Trans. Sig. Process VL - 59 UR - https://doi.org/10.1109/TSP.2011.2107904 DO - 10.1109/TSP.2011.2107904 ID - Khajehnejad2011 ER - TY - JOUR AU - Tropp, J. A. AU - Gilbert, A. C. AU - Strauss, M. J. PY - 2006 DA - 2006// TI - Algorithms for simultaneous sparse approximation: part i: greedy pursuit JO - Sig. Process. VL - 86 UR - https://doi.org/10.1016/j.sigpro.2005.05.030 DO - 10.1016/j.sigpro.2005.05.030 ID - Tropp2006 ER - TY - JOUR AU - Tropp, J. A. AU - Gilbert, A. C. PY - 2007 DA - 2007// TI - Signal recovery from random measurements via orthogonal matching pursuit JO - IEEE Trans. Inf. Theory VL - 53 UR - https://doi.org/10.1109/TIT.2007.909108 DO - 10.1109/TIT.2007.909108 ID - Tropp2007 ER - TY - JOUR AU - Needell, D. AU - Vershynin, R. PY - 2010 DA - 2010// TI - Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit JO - IEEE J. Sel. Top. Signal. Process VL - 4 UR - https://doi.org/10.1109/JSTSP.2010.2042412 DO - 10.1109/JSTSP.2010.2042412 ID - Needell2010 ER - TY - JOUR AU - Needell, D. AU - Tropp, J. A. PY - 2008 DA - 2008// TI - CoSaMP: iterative signal recovery from incomplete and inaccurate samples JO - Appl. Comput. Harmon. Anal. VL - 26 UR - https://doi.org/10.1016/j.acha.2008.07.002 DO - 10.1016/j.acha.2008.07.002 ID - Needell2008 ER - TY - JOUR AU - Candès, E. J. PY - 2008 DA - 2008// TI - The restricted isometry property and its implications for compressed sensing JO - Compte Rendus de l’Academie des Sciences. Paris, France, ser. I VL - 346 ID - Candès2008 ER - TY - JOUR AU - Baraniuk, R. AU - Davenport, M. AU - DeVore, D. AU - Wakin, M. PY - 2008 DA - 2008// TI - A simple proof of the restricted isometry property for random matrices JO - Constr. Approx. VL - 28 UR - https://doi.org/10.1007/s00365-007-9003-x DO - 10.1007/s00365-007-9003-x ID - Baraniuk2008 ER - TY - JOUR AU - Bayati, M. AU - Montanari, A. PY - 2012 DA - 2012// TI - The LASSO risk for Gaussian matrices JO - IEEE Trans. Inf. Theory VL - 58 UR - https://doi.org/10.1109/TIT.2011.2174612 DO - 10.1109/TIT.2011.2174612 ID - Bayati2012 ER - TY - JOUR AU - Ji, S. AU - Xue, Y. AU - Carin, L. PY - 2008 DA - 2008// TI - Bayesian compressive sensing JO - IEEE Trans. Signal Process VL - 56 UR - https://doi.org/10.1109/TSP.2007.914345 DO - 10.1109/TSP.2007.914345 ID - Ji2008 ER - TY - STD TI - EP Simoncelli, Modeling the joint statistics of images in the wavelet domain. Proc. SPIE. 3813:, 188–195 (1999). https://doi.org/10.1117/12.366779. ID - ref37 ER - TY - JOUR AU - Figueiredo, M. A. T. AU - Nowak, R. PY - 2001 DA - 2001// TI - Wavelet-based image estimation: an empirical bayes approach using Jeffreys’ noninformative prior JO - IEEE Trans. Image Process VL - 10 UR - https://doi.org/10.1109/83.941856 DO - 10.1109/83.941856 ID - Figueiredo2001 ER - TY - STD TI - H-Y Gao, Wavelet shrinkage denoising using the non-negative garrote. J. Comput. Graph. Stat. 7(4), 469–488 (1998). https://www.tandfonline.com/doi/abs/10.1080/10618600.1998.10474789. UR - https://www.tandfonline.com/doi/abs/10.1080/10618600.1998.10474789 ID - ref39 ER - TY - STD TI - L Sendur, IW Selesnick, Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. Trans. Sig. Proc.50(11), 2744–2756 (2002). https://doi.org/10.1109/TSP.2002.804091. ID - ref40 ER - TY - STD TI - DL Donoho, De-noising by soft-thresholding. IEEE Trans. Inf. Theor.41(3), 613–627 (1995). https://doi.org/10.1109/18.382009. ID - ref41 ER - TY - JOUR AU - Combettes, P. L. AU - Wajs, V. R. PY - 2005 DA - 2005// TI - Signal recovery by proximal forward-backward splitting JO - Multiscale Model. Simul VL - 4 UR - https://doi.org/10.1137/050626090 DO - 10.1137/050626090 ID - Combettes2005 ER - TY - STD TI - K Bredies, D Lorenz, Linear convergence of iterative soft-thresholding. J. Fourier Anal. Appl. 14(5-6), 813–837 (2008). https://doi.org/10.1007/s00041-008-9041-1. ID - ref43 ER - TY - CHAP AU - Montanari, A. ED - Eldar, Y. ED - Kutyniok, G. PY - 2012 DA - 2012// TI - Graphical models concepts in compressed sensing BT - Compressed Sensing: Theory and Applications PB - Cambridge University Press CY - Cambridge UR - https://doi.org/10.1017/CBO9780511794308.010 DO - 10.1017/CBO9780511794308.010 ID - Montanari2012 ER - TY - STD TI - EJ Candes, MB Wakin, SP Boyd, Enhancing sparsity by reweighted L1 minimization. Technical report. ID - ref45 ER - TY - JOUR AU - Friedlander, M. P. AU - Mansour, H. AU - Saab, R. AU - Yilmaz, O. PY - 2012 DA - 2012// TI - Recovering compressively sampled signals using partial support information JO - IEEE Trans. Inf. Theory VL - 58 UR - https://doi.org/10.1109/TIT.2011.2167214 DO - 10.1109/TIT.2011.2167214 ID - Friedlander2012 ER - TY - STD TI - MA Khajehnejad, W Xu, AS Avestimehr, B Hassibi, in Proceedings of the 2009 IEEE International Conference on Symposium on Information Theory - Volume 1, ISIT’09. Weighted L1 Minimization for Sparse Recovery with Prior Information (IEEE PressPiscataway, 2009), pp. 483–487. http://dl.acm.org/citation.cfm. UR - http://dl.acm.org/citation.cfm ID - ref47 ER - TY - STD TI - H Mansour, in 2012 IEEE Statistical Signal Processing Workshop (SSP) (SSP’12). Beyond ℓ1 norm minimization for sparse signal recovery (IEEEAnn Arbor, 2012). IEEE. ID - ref48 ER - TY - JOUR AU - Vila, J. P. AU - Schniter, P. PY - 2013 DA - 2013// TI - Expectation-maximization Gaussian-mixture approximate message passing JO - IEEE Trans. Sig. Process VL - 61 UR - https://doi.org/10.1109/TSP.2013.2272287 DO - 10.1109/TSP.2013.2272287 ID - Vila2013 ER - TY - JOUR AU - Guo, C. AU - Davies, M. E. PY - 2015 DA - 2015// TI - Near optimal compressed sensing without priors: parametric sure approximate message passing JO - IEEE Trans. Sig. Process VL - 63 UR - https://doi.org/10.1109/TSP.2015.2408569 DO - 10.1109/TSP.2015.2408569 ID - Guo2015 ER - TY - JOUR AU - Ravazzi, C. AU - Fosson, S. M. AU - Magli, E. PY - 2016 DA - 2016// TI - Randomized algorithms for distributed nonlinear optimization under sparsity constraints JO - IEEE Trans. Sig. Process VL - 64 UR - https://doi.org/10.1109/TSP.2015.2500887 DO - 10.1109/TSP.2015.2500887 ID - Ravazzi2016 ER - TY - JOUR AU - Wainwright, M. J. PY - 2009 DA - 2009// TI - Sharp thresholds for high-dimensional and noisy sparsity recovery using ℓ1-constrained quadratic programming (Lasso) JO - IEEE Trans. Inf. Theory VL - 55 UR - https://doi.org/10.1109/TIT.2009.2016018 DO - 10.1109/TIT.2009.2016018 ID - Wainwright2009 ER - TY - STD TI - C Chen, J Huang, L He, H Li, Fast iteratively reweighted least squares algorithms for analysis-based sparsity reconstruction. CoRR.1–14 (2014). https://arxiv.org/abs/1411.5057. UR - https://arxiv.org/abs/1411.5057 ID - ref53 ER - TY - JOUR AU - Ravazzi, C. AU - Magli, E. PY - 2015 DA - 2015// TI - Gaussian mixtures based IRLS for sparse recovery with quadratic convergence JO - IEEE Trans. Sig. Process VL - 63 UR - https://doi.org/10.1109/TSP.2015.2428216 DO - 10.1109/TSP.2015.2428216 ID - Ravazzi2015 ER - TY - STD TI - A Maleki. Approximate message passing algorithms for compressed sensing (Stanford University PhD thesis, 2011). https://www.ece.rice.edu/~mam15/suthesis-Arian.pdf. UR - https://www.ece.rice.edu/~mam15/suthesis-Arian.pdf ID - ref55 ER - TY - STD TI - M Raginsky, S Jafarpour, ZT Harmany, RF Marcia, RM Willett, R Calderbank, Performance bounds for expander-based compressed sensing in poisson noise. IEEE Trans. Sig. Process. 59(9), 4139–4153 (2011). https://doi.org/10.1109/TSP.2011.2157913. ID - ref56 ER - TY - STD TI - M Raginsky, RM Willett, ZT Harmany, RF Marcia, Compressed sensing performance bounds under poisson noise. IEEE Trans. Sig. Process. 58(8), 3990–4002 (2010). https://doi.org/10.1109/TSP.2010.2049997. ID - ref57 ER - TY - STD TI - ZT Harmany, RF Marcia, RM Willett, This is spiral-tap: sparse poisson intensity reconstruction algorithms? Theory and practice. IEEE Trans. Image Process. 21(3), 1084–1096 (2012). https://doi.org/10.1109/TIP.2011.2168410. ID - ref58 ER - TY - STD TI - R Gribonval, G Chardon, L Daudet, in IEEE International Conference on Acoustics, Speech, and Signal Processing, Blind calibration for compressed sensing by convex optimization (Kyoto, 2012). https://hal.inria.fr/hal-00658579. Accessed 25-30 Mar 2012. UR - https://hal.inria.fr/hal-00658579 ID - ref59 ER - TY - JOUR AU - Zhang, Z. AU - Jung, T. AU - Makeig, S. AU - Pi, Z. AU - Rao, B. D. PY - 2014 DA - 2014// TI - Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals JO - IEEE Trans. Neural Syst. Rehabil. Eng VL - 22 UR - https://doi.org/10.1109/TNSRE.2014.2319334 DO - 10.1109/TNSRE.2014.2319334 ID - Zhang2014 ER - TY - JOUR AU - Liu, S. AU - Zhang, Y. D. AU - Shan, T. AU - Qin, S. AU - Amin, M. G. PY - 2016 DA - 2016// TI - Structure-aware Bayesian compressive sensing for frequency-hopping spectrum estimation JO - Proc. SPIE VL - 9857 ID - Liu2016 ER - TY - STD TI - L He, L Carin, Exploiting structure in wavelet-based Bayesian compressive sensing. IEEE Trans. Sig. Process. 57:, 3488–3497 (2009). https://doi.org/10.1109/TSP.2009.2022003. ID - ref62 ER - TY - STD TI - RE Carrillo, AB Ramirez, GR Arce, KE Barner, BM Sadler, Robust compressive sensing of sparse signals: a review. EURASIP J. Adv. Sig. Process. 2016(1), 108 (2016). https://doi.org/10.1186/s13634-016-0404-5. ID - ref63 ER -