[1] DONOHO D. Compressed sensing [J]. IEEE transactions on information theory, 2006, 52(4): 1289-1306. [2] CANDES E, TAO T. Near-optimal signal recovery from random projections: universal encoding strategies? [J]. IEEE transactions on information theory, 2006, 52(12): 5406-5425. [3] 戴琼海,付长军,季向阳.压缩感知研究[J].计算机学报,2011,34(3):3425-3434.(DAI Q H, FU C J, JI X Y. Research on compressed sensing [J]. Chinese journal of computers, 2011, 34(3): 3425-3434.) [4] MALLAT S, ZHANG Z. Matching pursuits with time-frequency dictionaries [J]. IEEE transactions on signal processing, 1993, 41(12): 3397-3415. [5] CHEN S, DONOHO D, SAUNDERS M. Atomic decomposition by basis pursuit [J]. SIAM journal on scientific computing, 1998, 20(1): 33-61. [6] 张宗念,李金徽,黄仁泰.迭代硬阈值压缩感知重构算法——IIHT[J].计算机应用,2011,31(8):2123-2125.(ZHANG Z N, LI J H, HUANG R T. IIHT: new improved iterative hard thresholding algorithm for compressive sensing [J]. Journal of computer applications, 2011, 31(8): 2123-2125.) [7] LI C, YIN W, ZHANG Y. TVAL3: TV minimization by augmented Lagrangian and alternating direction algorithms [EB/OL]. [2014-11-07]. http://www.caam.rice.edu/~optimization/L1/TVAL3/. [8] BUADES A, COLL B, MOREL J. A non-local algorithm for image denoising [C]// CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2005, 2: 60-65. [9] DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering [J]. IEEE transactions on image processing, 2007, 16(8): 2080-2095. [10] 沈燕飞,李锦涛,朱珍民,等.基于非局部相似模型的压缩感知图像恢复算法[J].自动化学报,2015,41(2):261-272.(SHEN Y F, LI J T, ZHU Z M, et al. Image reconstruction algorithm of compressed sensing based on nonlocal similarity model [J]. Acta automatica sinica, 2015, 41(2): 261-272.) [11] DONG W, ZHANG L, SHI G, et al. Nonlocal centralized sparse representation for image restoration [J]. IEEE transactions on image processing, 2013, 4(22): 1620-1630. [12] ZHANG J, ZHAO D, GAO W. Group-based sparse representation for image restoration [J]. IEEE transactions on image processing, 2014, 8(23): 3336-3351. [13] ZHANG J, ZHAO D, ZHAO C, et al. Image compressive sensing recovery via collaborative sparsity [J]. IEEE journal on emerging and selected topics in circuits and systems, 2012, 2(3): 380-391. [14] 张选德,冯象初,王卫卫,等.求同存异的非局部图像去噪[J].中国科学:信息科学,2013,43(7):907-919.(ZHANG X D, FENG X C, WANG W W, et al. Exploit the similarity while preserving the difference for nonlocal image denoising [J]. Scientia sinica: informationis, 2013, 43(7): 907-919.) [15] RECHT B, FAZEL M, PARRILO P. Guaranteed minimum rank solutions of linear matrix equations via nuclear norm minimization [J]. SIAM review, 2010, 52(3): 471-501. [16] CAI J, CANDES E, SHEN Z, et al. A singular value thresholding algorithm for matrix completion [J]. SIAM journal of optimization, 2010, 20(4): 1956-1982. [17] GU S, ZHANG L, FENG X, et al. Weighted nuclear norm minimization with application to image denoising [C]// CVPR 2014: Proceedings of the 2014 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway, NJ: IEEE, 2014: 2862-2869. [18] JUSTIN R. Compressive sensing by random convolution [J]. SIAM journal on imaging sciences, 2009, 2(4): 1098-1128. [19] LIN Z, CHEN M, MA Y. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices [EB/OL]. [2014-10-18]. http://arxiv.org/abs/1009.5055. [20] CHEN C, TRAMEL E, FOWLER J. Compressed sensing recovery of images and video using multi-hypothesis predictions [C]// Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers. Piscataway, NJ: IEEE, 2011: 1193-1198. [21] EGIAZARIAN K, FOI A, KATKOVNIK V. Compressed sensing image reconstruction via recursive spatially adaptive filtering [C]// ICIP 2007: Proceedings of the 2007 IEEE International Conference on Image Processing. Washington, D.C.: IEEE Computer Society, 2007: 549-552. |