2. Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[3]
3. Candes E, Roberg J, Tao T. Robust uncertainty principles: exact signal recognition from highly incomplete frequency information. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
5. Takhar D, Laska J N, Wakin M B, et al. A new compressive imaging camera architecture using optical domain compression//Proceedings of the 2006 IS&T/SPIE Symposium on Electronic Imaging: Computational Imaging. San Jose, California, United States: SPIE, 2006, 6065: 43-52.
9. Liu Y, Cai J F, Zhan Z, et al. Balanced sparse model for tight frames in compressed sensing magnetic resonance imaging. PLoS One, 2015, 10(4): 1-19.
[10]
10. Huang Jinhong, Guo Li, Feng Qianjin, et al. Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data. Phys Med Biol, 2015, 60(14): 5359-5380.
[11]
11. Lustig M, Donoho D, Pauly J M. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med, 2007, 58(6): 1182-1195.
[12]
12. Ravishankar S, Bresler Y. MR image reconstruction from highly undersampled space data by dictionary learning. IEEE Trans Med Imaging, 2011, 30(5): 1028-1041.
[13]
13. Ravishankar S, Bresler Y. Sparsifing transform learning for compressed sensing MRI//IEEE International Symposium on Biomedical Imaging: From Nano to Macro. San Francisco, USA: IEEE, 2013: 17-20.
[14]
14. Ravishankar S, Bresler Y. Efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to magnetic resonance imaging. SIAM J Imaging Sci, 2015, 8(4): 2519-2557.
[15]
15. Wang X Y, Guo X, Zhang D D. An effective fractal image compression algorithm based on plane fitting. Chin Phys B, 2012, 21(9): 090507.
[16]
16. 宁方立, 何碧静, 韦娟. 基于 l p 范数的压缩感知图像重建算法研究. 物理学报, 2013, 62(17): 42121-42128.
[17]
17. Yaghoobi M, Nam S, Gribonval R, et al. Constrained over-complete analysis operator learning for co-sparse signal modeling. IEEE Transactions on Signal Processing, 2013, 61(9): 2141-2355.
[18]
18. Hawe S, Kleinsteuber M, Diepold K. Analysis operator learning and its application to image reconstruction. IEEE Transactions on Image Processing, 2013, 22(6): 2138-2150.
[19]
19. Chen Yunjin, Ranftl R, Pock T. Insights into analysis operator learning: from patch-based sparse models to higher order MRFs. IEEE Transactions on Image Processing, 2014, 23(3): 1060-1072.
[20]
20. Giryes R, Nam S, Elad M, et al. Greedy-like algorithms for the co-sparse analysis model. Linear Algebra Appl, 2014, 441: 22-60.
[21]
21. Rubinstein R, Peleg T, Elad M. Analysis K-SVD: A dictionary-learning algorithm for the analysis sparse model. IEEE Transactions on Signal Processing, 2013, 61(3): 661-677.
[22]
22. Ravishankar S, Bresler Y. Learning sparsifying transforms. IEEE Transactions on Signal Processing, 2013, 61(5): 1072-1086.
[23]
23. Eksioglu E M, Bayir O. K-SVD meets transform learning: transform K-SVD. IEEE Signal Process Lett, 2014, 21(3): 347-351.
