[1] [1] DESHPANDE S D, ER M H, VENKATESWARLU R, et al. Max-mean and max-median filters for detection of small targets［J］.Proceedings of the SPIE， 1999, 3809: 74-83.

[2] [2] BAI X, ZHOU F. Analysis of different modified top-hat transformations based on structuring element construction［J］. Signal Processing, 2010, 90(11): 2999-3003.

[3] [3] CHEN C L P, LI H, WEI Y, et al. A local contrast method for small infrared target detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 574-581.

[4] [4] ZHU J H, ZHANG B H,GU Y, et al. Infrared small target detection algorithm based on double neighborhood contrast measure［J］. Laser Technology, 2021, 45(6): 794-798(in Chinese).

[5] [5] HAN J H, WEI Y T, PENG Zh M, et al. Infrared dim and small target detection: A review ［J］. Infrared and Laser Engineering, 2022,51(4):20210393(in Chinese).

[6] [6] CANDS E J, LI X, MA Y, et al. Robust principal component analysis?［J］. Journal of the ACM, 2011, 58(3): 1-37.

[7] [7] GAO Ch Q, MENG D, YANG Y, et al. Infrared patch-image model for small target detection in a single image［J］. IEEE Transactions on Image Processing, 2013, 22(12): 4996-5009.

[8] [8] WANG X Y, PENG Zh M, KONG D H, et al. Infrared dim target detection based on total variation regularization and principal component pursuit［J］. Image and Vision Computing, 2017, 63: 1-9.

[9] [9] DAI Y M, WU Y Q, SONG Y, et al. Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values［J］. Infrared Physics & Technology, 2017, 81: 182-194.

[10] [10] DAI Y M, WU Y Q. Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3752-3767.

[11] [11] KILMER M E, MARTIN C D. Factorization strategies for third-order tensors［J］. Linear Algebra and Its Applications, 2011, 435(3): 641-658.

[12] [12] LU C, FENG J, CHEN Y, et al. Tensor robust principal component analysis with a new tensor nuclear norm［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(4): 925-938.

[13] [13] ZHANG X Y, DING Q H, LUO H B, et al. Infrared small target detection based on an image-patch tensor model［J］. Infrared Phy-sics & Technology, 2019, 99: 55-63.

[14] [14] ZHANG L D, PENG Zh M. Infrared small target detection based on partial sum of the tensor nuclear norm［J］. Remote Sensing, 2019, 11(4): 382.

[15] [15] SUN Y, YANG J G, LONG Y L, et al. Infrared patch-tensor model with weighted tensor nuclear norm for small target detection in a single frame［J］. IEEE Access, 2018, 6: 76140-76152.

[16] [16] ZHANG P, ZHANG L, WANG X, et al. Edge and corner awareness-based spatial-temporal tensor model for infrared small-target detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(12): 10708-10724.

[17] [17] LIU H K, ZHANG L, HUANG H. Small target detection in infrared videos based on spatio-temporal tensor model［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(12): 8689-8700.

[18] [18] PANG D D, SHAN T, LI W, et al. Facet derivative-based multidirectional edge awareness and spatial-temporal tensor model for infrared small target detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5001015.

[19] [19] HALKO N, MARTINSSON P G, TROPP J A. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions［J］. SIAM Review, 2011, 53(2): 217-288.

[20] [20] ZHANG J, SAIBABA A K, KILMER M E, et al. A randomized tensor singular value decomposition based on the t-product［J］. Nume-rical Linear Algebra with Applications, 2018, 25(5): e2179.

[21] [21] TARZANAGH D A, MICHAILIDIS G. Fast randomized algorithms for t-product based tensor operations and decompositions with applications to imaging data［J］. SIAM Journal on Imaging Sciences, 2018, 11(4): 2629-2664.

[22] [22] BULUC A, KOLDA T G, WILD S M, et al. Randomized algorithms for scientific computing［EB/OL］.(2022-03-21)［2023-01-12］.https://arxiv.53yu.com/abs/2104.11079.

[23] [23] KTHE U. Edge and junction detection with an improved structure tensor［C］//Joint Pattern Recognition Symposium. Berlin, Heidelberg, Germany: Springer, 2003: 25-32.

[24] [24] JIANG T X, HUANG T Z, ZHAO X L, et al. Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm［J］. Journal of Computational and Applied Mathematics, 2020, 372: 112680.

[25] [25] KOLDA T G, BADER B W. Tensor decompositions and applications［J］. SIAM Review, 2009, 51(3): 455-500.

[26] [26] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers［J］. Foundations and Trends in Machine Learning, 2011, 3(1): 1-122.

[27] [27] CANDES E J, WAKIN M B, BOYD S P. Enhancing sparsity by reweighted 1 minimization［J］. Journal of Fourier Analysis and Applications, 2008, 14(5): 877-905.

[28] [28] CHE M, WANG X Zh, WEI Y M, et al. Fast randomized tensor singular value thresholding for low-rank tensor optimization［J］. Numerical Linear Algebra with Applications, 2022: 29(6): e2444.

[29] [29] ZHANG Y, SHAO Y, SHEN J, et al. Infrared image impulse noise suppression using tensor robust principal component analysis and truncated total variation［J］. Applied Optics, 2021, 60(16): 4916-4929.

[30] [30] van LOAN C F, GOLUB G H. Matrix computations[M].4th ed. Baltimore MD, USA：The Johns Hopkins University Press, 2013：488-493.

[31] [31] HUI B W, SONG Zh Y, FAN H Q, et al. A dataset for infrared image dim-small aircraft target detection and tracking under ground / air background ［DB/OL］. (2019-10-28)［2023-04-03］.https://www.scidb.cn/en/detail?dataSetId=720626 4209334 59968&version=V1&dataSetType=journal&tag=2&language=zh_CN.