The detection of infrared dim and small targets is one of the key technologies in the infrared warning system. It remains challenging to accurately, quickly, and robustly detect dim and small targets. This paper proposes an infrared dim and small target detection algorithm based on low-rank and reweighted sparse representation. The algorithm formulates a new optimization equation to more accurately describe the rank of the background matrix and utilizes the structure tensor to extract local prior information. Experimental results show that the proposed algorithm improves the accuracy, speed, and robustness of detecting dim and small infrared targets.
YANG Yadong, HUANG Shengyi, TAN Yihua
. Infrared Dim and Small Target Detection Algorithm Based on Low-Rank and Reweighted Sparse Representation[J]. Journal of Applied Sciences, 2023
, 41(5)
: 753
-765
.
DOI: 10.3969/j.issn.0255-8297.2023.05.003
[1] Mao X, Diao W H. Criterion to evaluate the quality of infrared small target images[J]. Journal of Infrared Millimeter & Terahertz Waves, 2009, 30(1):56-64.
[2] Tom V T, Peli T, Leung M, et al. Morphology-based algorithm for point target detection in infrared backgrounds[C]//Signal and Data Processing of Small Targets 1993. International Society for Optics and Photonics, 1993, 1954:2-11.
[3] Deshpande S D, Er M H, Venkateswarlu R, et al. Max-mean and max-median filters for detection of small targets[C]//Signal and Data Processing of Small Targets 1999. International Society for Optics and Photonics, 1999, 3809:74-83.
[4] Shao X, Fan H, Lu G, et al. An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system[J]. Infrared Physics & Technology, 2012, 55(5):403-408.
[5] 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.
[6] Cao X, Rong C, Bai X. Infrared small target detection based on derivative dissimilarity mea sure[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8):3101-3116.
[7] Pan S D, Zhang S, Zhao M, et al. Infrared small target detection based on double-layer local contrast measure[J]. Acta Photonica Sinica, 2020, 49(1):0110003.
[8] Du P, Hamdulla A. Infrared moving small-target detection using spatial-temporal local dif ference measure[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10):1817-1821.
[9] Gao C, 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.
[10] Dai Y, Wu Y, Song Y. Infrared small target and background separation via column-wise weighted robust principal component analysis[J]. Infrared Physics & Technology, 2016, 77:421-430.
[11] Dai Y, Wu Y. 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.
[12] Zhang L, Peng Z. Infrared small target detection based on partial sum of the tensor nuclear norm[J]. Remote Sensing, 2019, 11(4):382.
[13] Zhu H, Liu S, Deng L, et al. Infrared small target detection via low-rank tensor completion with top-hat regularization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(2):1004-1016.
[14] Zhang T, Peng Z, Wu H, et al. Infrared small target detection via self-regularized weighted sparse model[J]. Neurocomputing, 2021, 420:124-148.
[15] Sun Y, Yang J, An W. Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 59(5):3737-3752.
[16] Zhou F, Wu Y, Dai Y. Infrared small target detection via incorporating spatial structural prior into intrinsic tensor sparsity regularization[J]. Digital Signal Processing, 2021, 111:102966.
[17] Kong X, Yang C, Cao S, et al. Infrared small target detection via nonconvex tensor fibered rank approximation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60:1-21.
[18] Kang Z, Peng C, Cheng Q. Robust PCA via nonconvex rank approximation[C]//2015 IEEE International Conference on Data Mining. IEEE, 2015:211-220.
[19] Baghaie A, Yu Z. Structure tensor based image interpolation method[J]. AEU-international Journal of Electronics and Communications, 2015, 69(2):515-522.
[20] Guo J, Wu Y, Dai Y. Small target detection based on reweighted infrared patch-image model[J]. IET Image Processing, 2017, 12(1):70-79.
[21] Zhang L, Peng L, Zhang T, et al. Infrared small target detection via non-convex rank ap proximation minimization joint l2, 1 norm[J]. Remote Sensing, 2018, 10(11):1821.
[22] Boyd S, Parikh N, Chu E. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends? in Machine Learning, 2011, 3(1):1-122.
[23] Dai Y, Wu Y, 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.
