[1] R Gade, T B Moeslund. Thermal cameras and applications: a survey. Machine Vision and Applications, 25, 245-262(2014).

[2] K Jiang, Z Wang, P Yi et al. ATMFN: Adaptive-threshold-based multi-model fusion network for compressed face hallucination. IEEE Transactions on Multimedia, 22, 2734-2747(2019).

[3] B C Arrue, A Ollero, J R M De Dios. An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems and their Applications, 15, 64-73(2000).

[4] J Zhang, X Zhou, L Li et al. A combined stripe noise removal and deblurring recovering method for thermal infrared remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

[5] A Liu, Y Liu, J Gu et al. Blind image super-resolution: A survey and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5461-5480(2022).

[6] X Wang, L Sun, A Chehri et al. A review of GAN-based super-resolution reconstruction for optical remote sensing images. Remote Sensing, 15, 5062(2023).

[7] O Rukundo, H Cao. Nearest neighbor value interpolation. arXiv preprint(2012).

[8] R Keys. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 29, 1153-1160(1981).

[9] H Chen, J Xue, S Zhang et al. Image super‐resolution based on adaptive cosparse regularization. Electronics Letters, 50, 1834-1836(2014).

[10] J L Gauvain, C H Lee. Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing, 2, 291-298(1994).

[11] C Dong, C C Loy, K He et al. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2015).

[12] C Ledig, L Theis, F Huszár et al. Photo-realistic single image super-resolution using a generative adversarial network, 4681-469(2017).

[13] Z Gao, J Chen. Maritime infrared image super-resolution using cascaded residual network and novel evaluation metric. IEEE Access, 10, 17760-17767(2022).

[14] K Choi, C Kim, M H Kang et al. Resolution improvement of infrared images using visible image information. IEEE Signal Processing Letters, 18, 611-614(2011).

[15] Shuo Huang, Yong Hu, Cai-Lan Gong et al. Salience region super-resolution reconstruction algorithm for infrared images based on sparse coding. Journal of Infrared and Millimeter Waves, 39, 388-395(2020).

[16] S Liu, Y Yang, Q Li et al. Infrared image super resolution using gan with infrared image prior, 1004-1009(2019).

[17] Bao-Tai Shao, Xin-Yi Tang, Lu Jin et al. Single frame infrared image super-resolution algorithm based on generative adversarial nets. Journal of Infrared and Millimeter Waves, 37, 427-432(2018).

[18] Y Huang, Z Jiang, R Lan et al. Infrared image super-resolution via transfer learning and PSRGAN. IEEE Signal Processing Letters, 28, 982-986(2021).

[19] Y Zou, L Zhang, C Liu et al. Super-resolution reconstruction of infrared images based on a convolutional neural network with skip connections. Optics and Lasers in Engineering, 146, 106717(2021).

[20] Wei Deng, Jian-Fei Chen, Sheng Zhang. Super-resolution reconstruction of thermal infrared image in deep residual network with skip connections. Electronics Optics & Control, 30, 27-32(2023).

[21] A Vaswani, N Shazeer, N Parmar et al. Attention is all you need. Advances in Neural Information Processing Systems, 30(2017).

[22] F Yang, H Yang, J Fu et al. Learning texture transformer network for image super-resolution, 5791-5800(2020).

[23] Y Xiao, Q Yuan, K Jiang et al. TTST: A top-k token selective transformer for remote sensing image super-resolution. IEEE Transactions on Image Processing, 30, 738-752(2024).

[24] Z Liu, Y Lin, Y Cao et al. Swin transformer: Hierarchical vision transformer using shifted windows, 10022(2021).

[25] T Salimans, D P Kingma. Weight normalization: A simple reparameterization to accelerate training of deep neural networks. Advances in Neural Information Processing Systems, 29(2016).

[26] Y Long, X Wang, M Xu et al. Dual self-attention Swin transformer for hyperspectral image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-12(2023).

[27] F Yang, H Yang, J Fu et al. Learning texture transformer network for image super-resolution, 5791-5800(2020).

[28] F Iandola, M Moskewicz, S Karayev et al. Densenet: Implementing efficient convnet descriptor pyramids. arXiv preprint(2014).

[29] S B Liang, K Song, W Zhao et al. DASR: Dual-attention transformer for infrared image super-resolution. Infrared Physics & Technology, 133, 104837(2023).

[30] Y Dai, F Gieseke, S Oehmcke et al. Attentional feature fusion, 3560-3569(2021).

[31] B Lim, S Son, H Kim et al. Enhanced deep residual networks for single image super-resolution, 136-144(2017).

[32] H Zhao, O Gallo, I Frosio et al. Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3, 47-57(2016).

[33] K Simonyan, A Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint(2014).

[34] A Jolicoeur-Martineau. The relativistic discriminator: a key element missing from standard GAN. arXiv preprint(2018).

[35] Y Han, J Liao, T Lu et al. KCPNet: Knowledge-driven context perception networks for ship detection in infrared imagery. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-19(2022).

[36] T Wu, B Li, Y Luo et al. MTU-Net: Multilevel TransUNet for space-based infrared tiny ship detection. IEEE Transactions on Geoscience and Remote Sensing, 61, 1-15(2023).

[37] M Heusel, H Ramsauer, T Unterthiner et al. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in Neural Information Processing Systems, 30(2017).

[38] C Y Wang, I H Yeh, H Y M Liao. Yolov9: Learning what you want to learn using programmable gradient information. arXiv preprint(2024).

[39] X Wang, K Yu, S Wu et al. ESRGAN: Enhanced super-resolution generative adversarial networks, 1-16(2018).

[40] A Li, L Zhang, Y Liu et al. Feature modulation transformer: Cross-refinement of global representation via high-frequency prior for image super-resolution, 12514-12524(2023).

[41] L Sun, J Dong, J Tang et al. Spatially-adaptive feature modulation for efficient image super-resolution, 13190-13199(2023).

[42] S Lei, Z Shi, W Mo. Transformer-based multistage enhancement for remote sensing image super-resolution. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-11(2021).

[43] F Li, Y Wu, H Bai et al. Learning detail-structure alternative optimization for blind super-resolution. IEEE Transactions on Multimedia, 25, 2825-2838(2022).