| [1] |
CANDES E , ROMBERG J , TAO T . Robust uncertainty princip es:exact signal reconstruction from highly incomplete frequency infor-mation[J]. IEEE Transactions on Information Theory, 2006,52(2): 489-509.
|
| [2] |
DONOHO D . Compressed sensing[J]. IEEE Transactions on Informa-tion Theory, 2006,52(4): 1289-1306.
|
| [3] |
ELDAR Y C , KUTYNIOK G . Compressed Sensing: Theory and Ap-plications[M]. USA: Cambridge University Press, 2012
|
| [4] |
LU G . Block compressed sensing of natural images[C]// The 15th International Conference on Digital Signal Processing. Cardiff, c2007:403-406.
|
| [5] |
THONG T D , TRAC D T , LU G . Fast compressive sampling with structurally random matrices[C]// The 2008 IEEE International Confe-rence on Acoustics, Speech and Signal Processing(ICASSP). Las Ve-gas, USA, c2008:3369-3372.
|
| [6] |
MUN S , FOWLER J . E.Block compressed sensing of images using directional transforms[C]// Data Compression Conference(DCC). Snowbird, Utah, c2010:547.
|
| [7] |
FOWLER J E , MUN S , TRAMEL E W . Block-based compressed sensing of images and video[J]. Foundations and Trends in Signal Processing, 2012(4):297-416.
|
| [8] |
THONG T D , LU G , NAM H N , et al. Fast and efficient compressive sensing using structurally random matrices[J]. IEEE Transactions on Signal Processing, 2012,60(1):139-154.
|
| [9] |
ARMIN E , HAN L Y , CHRISTOPHER J R , et al. The restricted iso-metry property for random block diagonal matrices[J]. Applied and Computational Harmonic Analysis, 2015,38(1):1-31.
|
| [10] |
VAN T C , DINH K Q , JEON B . Edge-preserving block compressive sensing with projected landweber[C]// The 20th International Confe-rence on Systems, Signals and Image Processing(IWSSIP). Bucharest, Romania, c2013:71-74.
|
| [11] |
FOUCART S , RAUHUT H . A Mathematical Introduction To Com-pressive Sensing[M]. Springer New York Press, USA. 2013.
|
| [12] |
LI C B . An Effcient Algorithm For Total Variation Regularization With Applications To The Single Pixel Camera And Compressive Sens-ing[D]. Rice University, 2009.
|
| [13] |
LI C B . Compressive Sensing For 3d Data Processing Tasks: Applica-tions, Models And Algorithms[D]. Rice University, 2011.
|
| [14] |
HE B S , TAO M , YUAN X M . A splitting method for separable con-vex programming[J]. IMA Journal of Numerical Analysis, 2014,(1):1-33.
|
| [15] |
HE B S , LIAO L Z , WANG X . Proximal-like contraction methods for monotone variational inequalities in a unified framework I: Effective quadruplet and primary methods[J]. Computation Optimization Ap-plication, 2012,(51):649-679.
|
| [16] |
HE B S , LIAO L Z , WANG X . Proximal-like contraction methods for monotone variational inequalities in a unified framework II: General methods and numerical experiments[J]. Computation Optimization Application, 2012,(51):681-708.
|
| [17] |
GU G Y , HE B S , YUAN X M . Customized proximal point a go-rithms for linearly constrained convex minimization a sad-dle-point problems: a unified approach[J]. Computation Optimization Application, 2014,(59):135-161.
|
