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

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

[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, 5, 29-56(2017).

[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, 25, 2337-2352(2016).

[5] 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, 55, 071014(2018).

[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, 13, 1250-1254(2016).

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

[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, 52, 4729-4743(2014).

[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, 54, 178-188(2016).

[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, 34, 2459-2466(2015).

[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, 35, 2117-2130(2013).

[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, 85, 10-20(2017).

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

[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, 133, 23-46(2005).

[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, 50, 3660-3677(2012).

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

[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, 37, 1751-1763(2015).

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

[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, 28, 1787-1800(2017).

[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, 35, 208-220(2013).

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

[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, 29, 3-19(2018).

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

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

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

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