Mallat, SG. A Wavelet Tour of Signal Processing. The Sparse Way: Academic Press; 2009.

Donoho, D. Compressed Sensing. IEEE Trans Inf Theory 2006, 52:1289–1306.

Candès, EJ, Romberg, J, Tao, T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 2006, 52:489–509.

Duarte, MF, Davenport, MA, Takhar, D, Laska, JN, Sun, T, Kelly, KF, Baraniuk, RG. Single‐pixel imaging via compressive sampling. IEEE Signal Process Mag 2008, 25:83–91.

Chan, WL, Charan, K, Takhar, D, Kelly, KF, Baraniuk, RG, Mittleman, DM. A single‐pixel terahertz imaging system based on compressed sensing. Appl Phys Lett 2008, 93:121105.

Stern, A, Javidi, B. Random projections imaging with extended space‐bandwidth product. J Display Technol 2007, 3:315–320.

Fergus, R, Torralba, A, Freeman, WT. Random lens imaging. *MIT CSAIL Technical Report*, 2006.

Marcia, RF, Willett, RM. Compressive coded aperture superresolution image reconstruction. *Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing*. 2007, 833–836.

Starck, J‐L, Candès, EJ, Donoho, DL. The curvelet transform for image denoising. IEEE Trans Image Process 2002, 11:670–684.

Elad, M, Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 2006, 15:3736–3745.

Mairal, J, Sapiro, G, Elad, M. Learning multiscale sparse representations for image and video restoration. Multiscale Model Simul 2008, 7:214–241.

Elad, M. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Haifa: Springer2010 edition; 2010.

Mairal, J, Elad, M, Sapiro, G. Sparse representation for color image restoration. IEEE Trans Image Process 2008, 17:53–69.

Guleryuz, OG. Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising‐part I: theory. IEEE Trans Image Process 2006, 15:539–554.

Fadili, MJ, Starck, J‐L, Murtagh, F. Inpainting and zooming using sparse representations. Comput J 2008, 52:64–79.

Candes, EJ, Plan, Y. Matrix completion with noise. Proc IEEE 2010, 98:925–936.

Gazit, S, Szameit, A, Eldar, YC, Segev, M. Super‐resolution and reconstruction of sparse sub‐wavelength images. Opt Express 2009, 17:23920–23946.

Glasner, D, Bagon, S, Irani, M. Super‐resolution from a single image. *Proceedings of the IEEE International Conference on Computer Vision*, 2009, 349–356.

Yang, J, Wright, J, Huang, TS, Ma, Y. Image super‐resolution via sparse representation. IEEE Trans Image Process 2010, 19:2861–2873.

Figueiredo, M, Bioucas‐Dias, JM, Nowak, RD. Majorization‐minimization algorithms for wavelet‐based image restoration. IEEE Trans Image Process 2007, 16:2980–2991.

Kennedy, RA, Samarasinghe, PD. Efficient blind separable kernel deconvolution for image deblurring. *2nd International Conference on Signal Processing and Communication Systems, 2008. (ICSPCS 2008)*, 2008, 1–7.

Bioucas‐Dias, JM. Bayesian wavelet‐based image deconvolution a GEM algorithm exploiting a class of heavy‐tailed priors. IEEE Trans Image Process 2006, 15:937–951.

Yu, G, Sapiro, G, Mallat, S. Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans Image Process 2012, 21:2481–2499.

Candès, EJ, Wakin, MB. An introduction to compressive sampling. IEEE Signal Process Mag 2008, 25:21–30.

Eldar, YC, Kutyniok, G. Compressed sensing: theory and applications. New York: Cambridge University Press; 2012.

Duarte, MF, Eldar, YC. Structured compressed sensing: from theory to applications. IEEE Trans Signal Process 2011, 59:4053–4085.

Marvasti, F, Amini, A, Haddadi, F, Soltanolkotabi, M, Khalaj, B, Aldroubi, A, Sanei, S, Chambers, J. A unified approach to sparse signal processing. EURASIP J Adv Signal Process, 2012, 2012:44.

Landsberg, JM. Tensors. Geometry and Applications. Providence, USA: American Mathematical Society; 2012.

Kolda, TG, Bader, BW. Tensor decompositions and applications. SIAM Rev 2009, 51:455–500.

De Lathauwer, L, De Moor, B, Vandewalle, J. A multilinear singular value decomposition. SIAM J Matrix Anal Appl 2000, 21:1253–1278.

Aldroubi, A, Cabrelli, C, Molter, U. Optimal non‐linear models for sparsity and sampling. J Fourier Anal Appl 2008, 14:793–812.

Borup, L, Nielsen, M, Gribonval, R. Nonlinear approximation with redundant dictionaries. IEEE Int Conf Acoust Speech Signal Process 2005, 4:iv–i4.

Aharon, M, Elad, M, Bruckstein, A. K‐SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 2006, 54:4311–4322.

Rubinstein, R, Bruckstein, AM, Elad, M. Dictionaries for sparse representation modeling. Proc IEEE 2010, 98:1045–1057.

Donoho, DL, Elad, M. Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. Proc Natl Acad Sci U S A 2003, 100:2197.

Candès, EJ, Tao, T. Near‐optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory 2006, 52:5406–5425.

Tropp, JA, Wright, SJ. Computational methods for sparse solution of linear inverse problems. Proc IEEE 2010, 98:948–958.

Tropp, JA. Greed is good: algorithmic results for sparse approximation. IEEE Trans Inf Theory 2004, 50:2231–2242.

Chen, SS, Donoho, DL, Saunders, MA. Atomic decomposition by basis pursuit. SIAM Rev 2001, 43:129–159.

Candès, EJ, Romberg, J, Tao, T. Stable signal recovery from incomplete and inaccurate measurements. *Transactions of the IRE Professional Group on Audio*, 2005.

Van Den Berg, E, Friedlander, MP. Probing the pareto frontier for basis pursuit solutions. SIAM J Sci Comput 2008, 31:890–912.

Van Den Berg, E, Friedlander, MP. *SPGL1: A Solver for Large‐Scale Sparse Reconstruction*. 2007. Available at: http://www.cs.ubc.ca/labs/scl/spgl1.

Rivenson, Y, Stern, A. Compressed imaging with a separable sensing operator. IEEE Signal Process Lett 2009, 16:449–452.

Davis, GM, Mallat, SG, Zhang, Z. Adaptive time‐frequency decompositions. Opt Eng 1994, 33:2183.

Tropp, JA, Gilbert, AC. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 2007, 53:4655–4666.

Needell, D, Tropp, JA. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmon Anal 2009, 26:301–321.

Rubinstein, R, Zibulevsky, M, Elad, M. Efficient Implementation of the K‐SVD Algorithm Using Batch Orthogonal Matching Pursuit. CS Technion, Haifa: Israel; 2008.

Candès, EJ, Tao, T. Decoding by linear programming. IEEE Trans Inf Theory 2005, 51:4203–4215.

Needell, D, Vershynin, R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. IEEE J Select Topic Signal Process 2014, 4:310–316.

Candes, EJ, Romberg, JK, T Tao,. Stable signal recovery from incomplete and inaccurate measurements. Comm Pure Appl Math, 2006, 59:1207–1223, .

Tucker, LR. Implications of factor analysis of three‐way matrices for measurement of Change. In: Harris, CW, ed. Measurement of Change. Madison: University of Wisconsin Press; 1963, 122–137.

Grasedyck, L, Kressner, D, Tobler, C. A literature survey of low‐rank tensor approximation techniques. arXiv.org, math, 2013.

Bergqvist, G, Larsson, E. The higher‐order singular value decomposition: theory and an application lecture notes. IEEE Signal Process Mag 2010, 27:151–154.

Da Costa, JPCL, Roemer, F, Haardt, M. Iterative sequential GSVD (IS‐GSVD) based prewhitening for multidimensional HOSVD based subspace estimation without knowledge of the noise covariance information. *2010 International ITG Workshop on Smart Antennas (WSA)*, 2010, 151–155.

Haardt, M, Roemer, F, Del Galdo, G. Higher‐order SVD‐based subspace estimation to improve the parameter estimation accuracy in multidimensional harmonic retrieval problems. IEEE Trans Signal Process 2008, 56:3198–3213.

Mørup, M. Applications of tensor (multiway array) factorizations and decompositions in data mining. WIREs: Data Mining Knowl Discov 2011, 1:24–40.

Nion, D, Sidiropoulos, D. Tensor algebra and multi‐dimensional harmonic retrieval in signal processing for MIMO radar. IEEE Trans Signal Process 2010, 58:1.

Miwakeichi, F, Martnez‐Montes, E, Valdés‐Sosa, PA, Nishiyama, N, Mizuhara, H, Yamaguchi, Y. Decomposing EEG data into space–time–frequency components using parallel factor analysis. Neuroimage 2004, 22:1035–1045.

Kolda, T, Dunlavy, D, Kegelmeyer, WP. Multilinear algebra for analyzing data with multiple linkages. *12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*, 2006.

Lim, L‐H, Comon, P. Multiarray signal processing: tensor decomposition meets compressed sensing. C R Mécanique 2010, 338:10–10.

Liu, J, Musialski, P, Wonka, P, Ye, J. Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell 2012, 35:208–220.

Gandy, S, Recht, B, Yamada, I. Tensor completion and low‐n‐rank tensor recovery via convex optimization. Inverse Probl 2011, 27:025010.

Sidiropoulos, ND, Kyrillidis, A. Multi‐way compressed sensing for sparse low‐rank tensors. IEEE Signal Process Lett 2012, 19:757–760.

Caiafa, CF, Cichocki, A. Block sparse representations of tensors using kronecker bases. *2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2012, 2709–2712.

Caiafa, CF, Cichocki, A. Computing sparse representations of multidimensional signals using kronecker bases. Neural Comput 2013, 25:186–220.

Van Den Berg, E, Friedlander, MP. Sparse optimization with least‐squares constraints. SIAM J Optim 2011, 21:1201–1229.

Rauhut, H. Circulant and toeplitz matrices in compressed sensing. *Transactions of the IRE Professional Group on Audio*. 2009, 1–7.

Do, TT, Tran, TD, Gan, L. Fast compressive sampling with structurally random matrices. *IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 (ICASSP 2008)*, 2008, 3369–3372.

Lustig, M, Donoho, DL, Santos, JM, Pauly, JM. Compressed sensing MRI. IEEE Signal Process Mag 2008, 25:72–82.

Haupt, J, Bajwa, WU, Raz, G, Nowak, R. Toeplitz compressed sensing matrices with applications to sparse channel estimation. IEEE Trans Inf Theory 2010, 56:5862–5875.

Rauhut, H, Romberg, J, Tropp, JA. Restricted isometries for partial random circulant matrices. ApplComput Harmon Anal. Time‐Frequency and Time‐Scale Analysis, Wavelets, Numerical Algorithms, and Applications 2012, 32:13–13.

Duarte, MF, Baraniuk, RG. Kronecker compressive sensing. IEEE Trans Image Process 2012, 21:494–504.

Robucci, R, Gray, JD, Romberg, LK, Chiu, J, Hasler, P. Compressive sensing on a CMOS separable‐transform image sensor. Proc IEEE, 2010, 98:1089–1101 .

Baraniuk, RG, Cevher, V, Duarte, MF, Hegde, C. Model‐based compressive sensing. IEEE Trans Inf Theory 2010, 56:1982–2001.

Crouse, MS, Nowak, RD, Baraniuk, RG. Wavelet‐based Statistical Signal Processing Using Hidden Markov Models. IEEE Trans Signal Process 1998, 46:886–902.

Eldar, YC, Mishali, M. Robust recovery of signals from a structured union of subspaces. IEEE Trans Inf Theory 2009, 55:5302–5316.

Eldar, Y, Kuppinger, P, Bolcskei, H. Block‐sparse signals: uncertainty relations and efficient recovery. IEEE Trans Signal Process 2011, 59:4053–4085.

Jokar, S, Mehrmann, V. Sparse solutions to underdetermined kronecker product systems. Linear Algebra Appl 2009, 431:2437–2447.

Donoho, DL. Wedgelets: Nearly minimax estimation of edges. Ann Stat 1999, 27:859–897.

Candès, EJ, Donoho, DL. Ridgelets: A key to higher‐dimensional intermittency? Philos Trans R Soc Lond A 1999, 357:2495–2509.

Candès, E, Demanet, L, Donoho, D, Ying, L. Fast discrete curvelet transforms. Multiscale Model Sim, 2006, 5:861–899.

Lu, Y, Do, MN. A new contourlet transform with sharp frequency localization. *Proceedings of the IEEE International Conference on Image Processing*, 2006, 1629–1632.

Le Pennec, E, Mallat, S. Sparse geometric image representations with bandelets. IEEE Trans Image Process 2005, 14:423–438.

Gribonval, R, Schnass, K. Dictionary identification: sparse matrix‐factorization via l1 ‐minimization. Trans Inf Theory 2010, 56:3523–3539.

Ravishankar, S, Bresler, Y. Learning sparsifying transforms. IEEE Trans Signal Process 2013, 61:1072–1086.

Engan, K, Rao, BD, Kreutz‐Delgado, K. Frame design using FOCUSS with method of optimal directions (MOD). *Norwegian Signal Processing Symposium*, 1999, 65–69.

Rubinstein, R, Zibulevsky, M, Elad, M. Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans Signal Process 2010, 58:1553–1564.

Hu, S, Lustig, M, Chen, AP, Crane, J, Kerr, A, Kelley, DAC, Hurd, R, Kurhanewicz, J, Nelson, SJ, Pauly, JM, et al. Compressed sensing for resolution enhancement of hyperpolarized 13C Flyback 3D‐MRSI. J Magn Reson 2008, 192:258–264.

Mitsouras, D, Mulkern, RV, Rybicki, FJ. Fast, exact k‐space sample density compensation for trajectories composed of rotationally symmetric segments, and the SNR‐optimized image reconstruction from non‐Cartesian samples. Magn Reson Med 2008, 60:339–349.

Trzasko, J, Manduca, A. Highly undersampled magnetic resonance image reconstruction via homotopic. IEEE Trans Med Imaging 2009, 28:106–121.

Seeger, M, Nickisch, H, Pohmann, R, Scholkopf, B. Optimization of k‐space trajectories for compressed sensing by Bayesian experimental design. Magn Reson Med 2009.

Miao, J, Guo, W, Narayan, S, Wilson, DL. A simple application of compressed sensing to further accelerate partially parallel imaging. Magn Reson Imaging 2013, 31:75–85.

Gamper, U, Boesiger, P, Kozerke, S. Compressed sensing in dynamic MRI. Magn Reson Med 2008, 59:365–373.

Lingala, SG, Hu, Y, DiBella, E, Jacob, M. Accelerated dynamic MRI exploiting sparsity and low‐rank structure: k‐t SLR. IEEE Trans Med Imaging 2011, 30:1042–1054.

Majumdar, A. Improved dynamic MRI reconstruction by exploiting sparsity and rank‐deficiency. Magn Reson Imaging 2012, 1–7:789–795.

Coifman, R, Geshwind, F, Meyer, Y. Noiselets. Appl Comput Harmon Anal 2001, 10:27–44.

Stern, A. Compressed imaging system with linear sensors. Opt Lett 2007, 32:3077–3079.

Foster, DH, Amano, K, Nascimento, SM, Foster, MJ. Frequency of metamerism in natural scenes. J Opt Soc Am A 2006, 23:2359–2372.

Acar, E, Dunlavy, DM, Kolda, TG, Mørup, M. Scalable tensor factorizations for incomplete data. Chemom Intell Lab Syst 2011, 106:41–56.

Dauwels, J, Garg, L, Earnest, A, Khai Pang, Leong. Handling missing data in medical questionnaires using tensor decompositions. *Communications and Signal Processing (ICICS) 2011 8th International Conference on Information*, 2011, 1–5.

Dauwels, J, Garg, L, Earnest, A, Khai Pang, L. Tensor factorization for missing data imputation in medical questionnaires. *2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2012, 2109–2112.

Tayyab Asif, M, Mitrovic, N, Garg, L, Dauwels, J, Jaillet, P. Low‐dimensional models for missing data imputation in road networks. *2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, 2013.

Tan, H, Feng, G, Feng, J, Wang, W, Zhang, Y‐J, Li, F. A tensor‐based method for missing traffic data completion. Transport Res Part C 2013, 28:15–27.

Kim, KI, Kwon, Y. Single‐image super‐resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 2010, 32:1127–1133.

Tang, Y, Yuan, Y, Yan, P, Li, X. Greedy regression in sparse coding space for single‐image super‐resolution. J Vis Commun Image Represent 2013, 24:148–159.

Nam, S, Davies, ME, Elad, M, Gribonval, R. The cosparse analysis model and algorithms. Appl Comput Harmon Anal 2013, 34:30–56.