[1] [1] ZHAO M, LI W, LI L, et al. Three-order tensor creation and tucker decomposition for infrared small-target detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2021,60: 1-16.

[2] [2] ZHAO M, LI L, LI W, et al. Infrared small-target detection based on multiple morphological profiles[J].IEEE Transactions on Geoscience and Remote Sensing, 2020,59(7): 6077-6091.

[3] [3] ZHANG J, TAO D. Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things[J].IEEE Internet of Things Journal, 2020,8(10): 7789-7817.

[4] [4] 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,Proc. of SPIE, 1999,3809: 74-83.

[5] [5] 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.

[6] [6] HAN J, Moradi S, Faramarzi I, et al. A local contrast method for infrared small-target detection utilizing a tri-layer window[J].IEEE Geoscience and Remote Sensing Letters, 2019,17(10): 1822-1826.

[7] [7] HAN J, Moradi S, Faramarzi I, et al. Infrared small target detection based on the weighted strengthened local contrast measure[J].IEEE Geoscience and Remote Sensing Letters, 2020,18(9): 1670-1674.

[8] [8] DAI Y, WU Y, ZHOU F, et al. Asymmetric contextual modulation for infrared small target detection[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021: 950-959.

[9] [9] DAI Y, WU Y, ZHOU F, et al. Attentional local contrast networks for infrared small target detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2021,59(11): 9813-9824.

[10] [10] ZHANG M, ZHANG R, YANG Y, et al. ISNet: Shape matters for infrared small target detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 877-886.

[11] [11] WU X, HONG D, Chanussot J. UIU-Net: U-Net in U-Net for infrared small object detection[J].IEEE Transactions on Image Processing, 2022,32: 364-376.

[12] [12] LI B, XIAO C, WANG L, et al. Dense nested attention network for infrared small target detection[J].IEEE Transactions on Image Processing, 2022,32: 1745-1758.

[13] [13] WANG K, WU X, ZHOU P, et al. AFE-Net: Attention-guided feature enhancement network for infrared small target detection[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024,17: 4208-4221.

[14] [14] ZHANG M, YUE K, ZHANG J, et al. Exploring feature compensation and cross-level correlation for infrared small target detection[C]//Proceedings of the30th ACM International Conference on Multimedia, 2022: 1857-1865.

[15] [15] LIU F, GAO C, CHEN F, et al. Infrared small and dim target detection with transformer under complex backgrounds[J].IEEE Transactions on Image Processing, 2023,32: 5921-5932.

[18] [18] Samuel D, Ben-Ari R, Raviv S, et al. Generating images of rare concepts using pre-trained diffusion models[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2024,38(5): 4695-4703.

[19] [19] LI Y, WANG H, JIN Q, et al. Snapfusion: text-to-image diffusion model on mobile devices within two seconds[J].Advances in Neural Information Processing Systems, 2023,36: 20662-20678.

[20] [20] ZHANG Z, LI B, NIE X, et al. Towards consistent video editing with text-to-image diffusion models[J].Advances in Neural Information Processing Systems, 2023,36: 58508-58519.

[21] [21] CHEN C F R, FAN Q, Panda R. Crossvit: cross-attention multi-scale vision transformer for image classification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 357-366.

[22] [22] HO J, JAIN A, Abbeel P. Denoising diffusion probabilistic models[J].Advances in Neural Information Processing Systems, 2020,33: 6840-6851.

[23] [23] Saharia C, Ho J, Chan W, et al. Image super-resolution via iterative refinement[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,45(4): 4713-4726.

[24] [24] XI Y, ZHANG J, LIU K. Nanetformer: nested attention network with auxiliary transformer enhancement for infrared small target detection[C]//IGARSS2023-2023IEEE International Geoscience and Remote Sensing Symposium, 2023: 6596-6599.

[25] [25] Zamir S W, Arora A, Khan S, et al. Restormer: efficient transformer for high-resolution image restoration[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 5728-5739.

[26] [26] ZHANG T, LI L, CAO S, et al. Attention-guided pyramid context networks for detecting infrared small target under complex background[C]//IEEE Transactions on Aerospace and Electronic Systems, 2023,59(4): 4250-4261. doi: 10.1109/TAES.2023.3238703

[27] [27] WANG Huan, ZHOU Luping, WANG Lei. Miss detection vs. false alarm: adversarial learning for small object segmentation in infrared images[C]//Proceedings of2019IEEE/CVF International Conference on Computer Vision(ICCV), 2019: 8508-8517. doi: 10.1109/ICCV.2019.00860.

[28] [28] ZHANG L, PENG L, ZHANG T, et al. Infrared small target detection via non-convex rank approximation minimization joint l 2, 1 norm[J].Remote Sensing, 2018,10(11): 1821.

[29] [29] ZHANG L, PENG Z. Infrared small target detection based on partial sum of the tensor nuclear norm[J].Remote Sensing, 2019,11(4): 382.

[30] [30] 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.

[31] [31] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.