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Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.
J. C. Gower, “Generalized procrustes analysis,” Psychometrika , vol. 40, no. 1, pp. 33–51, 1975
1975
Earlier work this paper cites.
L. Bottou, “On-line learning in neural networks.” New York, NY, USA: Cambridge Univ Pr, 1998, ch. Online learning and stochastic approximations, pp. 9–42
1998
Earlier work this paper cites.
K. Engan, S. Aase, and J. Hakon-Husoy, “Method of optimal directions for frame design,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing , 1999, pp. 2443–2446
1999
Earlier work this paper cites.
N. Rajpoot, Z. Yao, and R. Wilson, “Adaptive wavelet restoration of noisy video sequences,” in International Conference on Image Processing , vol. 2, 2004, pp. 957–960 Vol.2
2004
Earlier work this paper cites.
D. Rusanovskyy and K. Egiazarian, “Video denoising algorithm in sliding 3D DCT domain,” in Advanced Concepts for Intelligent Vision Systems: 7th International Conference, ACIVS 2005 , 2005, pp. 618–625
2005
Earlier work this paper cites.
P. L. Combettes and V. R. Wajs, “Signal recovery by proximal forward-backward splitting,” Multiscale Modeling & Simulation , vol. 4, no. 4, pp. 1168–1200, 2005
2005
Earlier work this paper cites.
M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on signal processing , vol. 54, no. 11, pp. 4311–4322, 2006
2006
Earlier work this paper cites.
M. Lustig, J. M. Santos, D. L. Donoho, and J. M. Pauly, “k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity,” in Proc. ISMRM , 2006, p. 2420
2006
Earlier work this paper cites.
M. Lustig, D. L. Donoho, and J. M. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magnetic Resonance in Medicine , vol. 58, no. 6, pp. 1182–1195, 2007
2007
Earlier work this paper cites.
Z. P. Liang, “Spatiotemporal imaging with partially separable functions,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2007, pp. 988–991
2007
Earlier work this paper cites.
B. Sharif and Y. Bresler, “Adaptive real-time cardiac MRI using paradise: Validation by the physiologically improved NCAT phantom,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2007, pp. 1020–1023
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
J. Mairal, M. Elad, and G. Sapiro, “Sparse representation for color image restoration,” IEEE Trans. on Image Processing , vol. 17, no. 1, pp. 53–69, 2008
2008
Earlier work this paper cites.
M. Yaghoobi, T. Blumensath, and M. Davies, “Dictionary learning for sparse approximations with the majorization method,” IEEE Transaction on Signal Processing , vol. 57, no. 6, pp. 2178–2191, 2009
2009
Earlier work this paper cites.
M. Protter and M. Elad, “Image sequence denoising via sparse and redundant representations,” IEEE Trans. on Image Processing , vol. 18, no. 1, pp. 27–36, 2009
2009
Earlier work this paper cites.
H. Jung, K. Sung, K. S. Nayak, E. Y. Kim, , and J. C. Ye, “k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI,” Magnetic Resonance in Medicine , vol. 61, no. 1, pp. 103–116, 2009
2009
Earlier work this paper cites.
H. Pedersen, S. Kozerke, S. Ringgaard, K. Nehrke, and W. Y. Kim, “k-t PCA: Temporally constrained k-t BLAST reconstruction using principal component analysis,” Magnetic Resonance in Medicine , vol. 62, no. 3, pp. 706–716, 2009
2009
Earlier work this paper cites.
M. Yaghoobi, T. Blumensath, and M. Davies, “Dictionary learning for sparse approximations with the majorization method,” IEEE Transaction on Signal Processing , vol. 57, no. 6, pp. 2178–2191, 2009
2009
Earlier work this paper cites.
J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. , vol. 11, pp. 19–60, 2010
2010
Earlier work this paper cites.
J. P. Haldar and Z. P. Liang, “Spatiotemporal imaging with partially separable functions: A matrix recovery approach,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2010, pp. 716–719
2010
Earlier work this paper cites.
B. Zhao, J. P. Haldar, C. Brinegar, and Z. P. Liang, “Low rank matrix recovery for real-time cardiac MRI,” in IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2010, pp. 996–999
2010
Earlier work this paper cites.
J. Mairal, F. Bach, J. Ponce, and G. Sapiro, “Online learning for matrix factorization and sparse coding,” Journal of Machine Learning Research , vol. 11, no. Jan, pp. 19–60, 2010
2010
Cited alongside, same era.
R. Gribonval and K. Schnass, “Dictionary identification–sparse matrix-factorization via l 1 \textit{l}_{1} -minimization,” IEEE Trans. Inform. Theory , vol. 56, no. 7, pp. 3523–3539, 2010
2010
Cited alongside, same era.
I. Ramirez, P. Sprechmann, and G. Sapiro, “Classification and clustering via dictionary learning with structured incoherence and shared features,” in Proc. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2010 , 2010, pp. 3501–3508
2010
Cited alongside, same era.
R. Rubinstein, M. Zibulevsky, and M. Elad, “Double sparsity: Learning sparse dictionaries for sparse signal approximation,” IEEE Transactions on Signal Processing , vol. 58, no. 3, pp. 1553–1564, 2010
2010
Cited alongside, same era.
C. Sutour, C. Deledalle, and J. Aujol, “Adaptive regularization of the NL-means: Application to image and video denoising,” IEEE Transactions on Image Processing , vol. 23, no. 8, pp. 3506–3521, Aug 2014
2014
Later among the works it cites.
J. Caballero, A. N. Price, D. Rueckert, and J. V. Hajnal, “Dictionary learning and time sparsity for dynamic mr data reconstruction,” IEEE Transactions on Medical Imaging , vol. 33, no. 4, pp. 979–994, 2014
2014
Later among the works it cites.
N. Parikh and S. Boyd, “Proximal algorithms,” Foundations and Trends® in Optimization , vol. 1, no. 3, pp. 127–239, 2014
2014
Later among the works it cites.
S. Ravishankar, B. Wen, and Y. Bresler, “Online sparsifying transform learning – Part I: Algorithms,” IEEE Journal of Selected Topics in Signal Processing , vol. 9, no. 4, pp. 625–636, 2015
2015
Later among the works it cites.
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K. Skretting and K. Engan, “Recursive least squares dictionary learning algorithm,” IEEE Transactions on Signal Processing , vol. 58, no. 4, pp. 2121–2130, 2010
2010
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “MR image reconstruction from highly undersampled k-space data by dictionary learning,” IEEE Trans. Med. Imag. , vol. 30, no. 5, pp. 1028–1041, 2011
2011
Cited alongside, same era.
J. Trzasko and A. Manduca, “Local versus global low-rank promotion in dynamic MRI series reconstruction,” in Proc. ISMRM , 2011, p. 4371
2011
Cited alongside, same era.
E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” J. ACM , vol. 58, no. 3, pp. 11:1–11:37, 2011
2011
Cited alongside, same era.
S. G. Lingala, Y. Hu, E. DiBella, and M. Jacob, “Accelerated dynamic MRI exploiting sparsity and low-rank structure: kt slr,” IEEE transactions on medical imaging , vol. 30, no. 5, pp. 1042–1054, 2011
2011
Cited alongside, same era.
S. Shalev-Shwartz et al. , “Online learning and online convex optimization,” Foundations and Trends® in Machine Learning , vol. 4, no. 2, pp. 107–194, 2012
2012
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “Learning sparsifying transforms,” IEEE Trans. Signal Process. , vol. 61, no. 5, pp. 1072–1086, 2013
2013
Cited alongside, same era.
——, “Learning doubly sparse transforms for images,” IEEE Transactions on Image Processing , vol. 22, no. 12, pp. 4598–4612, 2013
2013
Cited alongside, same era.
S. Ravishankar and Y. Bresler, “Online sparsifying transform learning – part II: Convergence analysis,” IEEE Journal of Selected Topics in Signal Processing , vol. 9, no. 4, pp. 637–646, 2015
2015
Later among the works it cites.
B. Wen, S. Ravishankar, and Y. Bresler, “Video denoising by online 3D sparsifying transform learning,” in 2015 IEEE International Conference on Image Processing (ICIP) , 2015, pp. 118–122
2015
Later among the works it cites.
R. Otazo, E. Candès, and D. K. Sodickson, “Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components,” Magnetic Resonance in Medicine , vol. 73, no. 3, pp. 1125–1136, 2015
2015
Later among the works it cites.
R. Otazo, “L+S reconstruction matlab code,” http://cai2r.net/resources/software/ls-reconstruction-matlab-code , 2014, [Online; accessed Mar. 2015]
2015
Later among the works it cites.
B. Wen, S. Ravishankar, and Y. Bresler, “Learning flipping and rotation invariant sparsifying transforms,” in 2016 IEEE International Conference on Image Processing , 2016, pp. 3857–3861
2016
Later among the works it cites.
S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Efficient learning of dictionaries with low-rank atoms,” in IEEE Global Conference on Signal and Information Processing (GlobalSIP) , December 2016, pp. 222–226
2016
Later among the works it cites.
H. Guo and N. Vaswani, “Video denoising via online sparse and low-rank matrix decomposition,” in IEEE Statistical Signal Processing Workshop (SSP) , June 2016, pp. 1–5
2016
Later among the works it cites.
——, “Data-driven learning of a union of sparsifying transforms model for blind compressed sensing,” IEEE Transactions on Computational Imaging , vol. 2, no. 3, pp. 294–309, 2016
2016
Later among the works it cites.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, July 2017
2017
Later among the works it cites.
S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging,” IEEE Transactions on Medical Imaging , vol. 36, no. 5, pp. 1116–1128, 2017
2017
Later among the works it cites.
B. E. Moore and S. Ravishankar, “Online data-driven dynamic image restoration using DINO-KAT models,” in IEEE International Conference on Image Processing , September 2017, pp. 3590–3594
2017
Later among the works it cites.
S. Ravishankar, B. E. Moore, R. R. Nadakuditi, and J. A. Fessler, “Efficient online dictionary adaptation and image reconstruction for dynamic MRI,” in Asilomar Conference on Signals, Systems, and Computers , October 2017, pp. 835–839
2017
Later among the works it cites.
S. Ravishankar, R. R. Nadakuditi, and J. A. Fessler, “Efficient sum of outer products dictionary learning (SOUP-DIL) and its application to inverse problems,” IEEE Transactions on Computational Imaging , vol. 3, no. 4, pp. 694–709, Dec 2017
2017
Later among the works it cites.
——, “VIDOSAT: High-dimensional sparsifying transform learning for online video denoising,” IEEE Transactions on Image Processing , vol. 28, no. 4, pp. 1691–1704, 2018
2018
Closest in time.
S. G. Lingala, “k-t SLR: MATLAB package,” http://user.engineering.uiowa.edu/~jcb/software/ktslr_matlab/Software.html , 2011, [Online; accessed Mar. 2015]
2018
Closest in time.
B. Dietz, B. G. Fallone, and K. Wachowicz, “Nomenclature for real-time magnetic resonance imaging,” Magnetic Resonance in Medicine , vol. 81, no. 3, pp. 1483–1484, 2019
2019
Closest in time.