[1] 刘立新, 李梦珠, 赵志刚, 等. 高光谱成像技术在生物医学中的应用进展[J]. 中国激光, 2018, 45(2): 0207017.

    Liu L X, Li M Z, Zhao Z G, et al. Recent advances of hyperspectral imaging application in biomedicine[J]. Chinese Journal of Lasers, 2018, 45(2): 0207017.

[2] 付立婷, 邓河, 刘春红. 新型高光谱图像快速实时目标检测与分类方法[J]. 光学学报, 2017, 37(2): 0230002.

    Fu L T, Deng H, Liu C H. Novel fast real-time target detection and classification algorithms for hyperspectral imagery[J]. Acta Optica Sinica, 2017, 37(2): 0230002.

[3] Yokoya N, Grohnfeldt C, Chanussot J. Hyperspectral and multispectral data fusion: a comparative review of the recent literature[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(2): 29-56.

[4] Dong W S, Fu F Z, Shi G M, et al. Hyperspectral image super-resolution via non-negative structured sparse representation[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2016, 25(5): 2337-2352.

[5] 许蒙恩, 谢宝陵, 徐国明. 空间光谱联合稀疏表示的高光谱图像超分辨率方法[J]. 激光与光电子学进展, 2018, 55(7): 071014.

    Xu M E, Xie B L, Xu G M. Hyperspectral image super-resolution method based on spatial spectral joint sparse representation[J]. Laser & Optoelectronics Progress, 2018, 55(7): 071014.

[6] Li J, Yuan Q Q, Shen H F, et al. Hyperspectral image super-resolution by spectral mixture analysis and spatial-spectral group sparsity[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(9): 1250-1254.

[7] Guo Z H, Wittman T, Osher S. L1 unmixing and its application to hyperspectral image enhancement[J]. Proceedings of SPIE, 2009, 7334: 73341M.

[8] Zhang H Y, He W, Zhang L P, et al. Hyperspectral image restoration using low-rank matrix recovery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4729-4743.

[9] He W, Zhang H Y, Zhang L P, et al. Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 178-188.

[10] Shi F, Cheng J, Wang L, et al. LRTV: MR image super-resolution with low-rank and total variation regularizations[J]. IEEE Transactions on Medical Imaging, 2015, 34(12): 2459-2466.

[11] Hu Y, Zhang D B, Ye J P, et al. Fast and accurate matrix completion via truncated nuclear norm regularization[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2013, 35(9): 2117-2130.

[12] Cao F L, Chen J Y, Ye H L, et al. Recovering low-rank and sparse matrix based on the truncated nuclear norm[J]. Neural Networks, 2017, 85: 10-20.

[13] Tao P D. An L T H. Convex analysis approach to d.c. programming: theory, algorithms and applications[J]. Acta Mathematica Vietnamica, 1997, 22(1): 289-355.

[14] An L T H, Tao P D. The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems[J]. Annals of Operations Research, 2005, 133: 23-46.

[15] Yuan Q Q, Zhang L P, Shen H F. Hyperspectral image denoising employing a spectral-spatial adaptive total variation model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 3660-3677.

[16] Kolda T G, Bader B W. Tensor decompositions and applications[J]. SIAM Review, 2009, 51(3): 455-500.

[17] Zhao Q B, Zhang L Q, Cichocki A. Bayesian CP factorization of incomplete tensors with automatic rank determination[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1751-1763.

[18] Tucker L R. Some mathematical notes on three-mode factor analysis[J]. Psychometrika, 1966, 31(3): 279-311.

[19] Li X T, Ng M K, Cong G, et al. MR-NTD: manifold regularization nonnegative tucker decomposition for tensor data dimension reduction and representation[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(8): 1787-1800.

[20] Liu J, Musialski P, Wonka P, et al. Tensor completion for estimating missing values in visual data[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 208-220.

[21] Gandy S, Recht B, Yamada I. Tensor completion and low-n-rank tensor recovery via convex optimization[J]. Inverse Problems, 2011, 27(2): 025010.

[22] Zhang C Y, Hu W R, Jin T Y, et al. Nonlocal image denoising via adaptive tensor nuclear norm minimization[J]. Neural Computing and Applications, 2018, 29(1): 3-19.

[23] Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60: 259-268.

[24] Boyd S. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends ^© in Machine Learning, 2010, 3(1): 1-122.

[25] Marquina A, Osher S J. Image super-resolution by TV-regularization and Bregman iteration[J]. Journal of Scientific Computing, 2008, 37(3): 367-382.

[26] AVIRIS data[DB/OL]. [2017-07-19].https:∥aviris. jpl. nasa. gov/data/image_cube. html.

[27] HYDICE data[DB/OL]. [2017-01-30].https:∥engineering purdue. edu/~biehl/MultiSpec/ hype- rspectral. html.

[28] Manjón J V, Coupé P, Buades A, et al. Non-local MRI upsampling[J]. Medical Image Analysis, 2010, 14(6): 784-792.