REFERENCES
1. Erden, F.; Çetin, A. E. Hand gesture based remote control system using infrared sensors and a camera. IEEE. Trans. Consum. Electron. 2014, 60, 675-80.
2. Gade, R.; Moeslund, T. B. Thermal cameras and applications: a survey. Mach. Vis. Appl. 2014, 25, 245-62.
3. Wijnhoven, R. G. J.; de With, P. H. N. Identity verification using computer vision for automatic garage door opening. IEEE. Trans. Consum. Electron. 2011, 57, 906-14.
4. Kang, J.; Anderson, D. V.; Hayes, M. H. Face recognition for vehicle personalization with near infrared frame differencing. IEEE. Trans. Consum. Electron. 2016, 62, 316-24.
5. Chen, C.; Xu, Y.; Yang, X. User tailored colorization using automatic scribbles and hierarchical features. Digit. Signal. Process. 2019, 87, 155-65.
6. Tang, Y.; Zhu, M.; Chen, Z.; et al. Seismic performance evaluation of recycled aggregate concrete-filled steel tubular columns with field strain detected via a novel mark-free vision method. Structures 2022, 37, 426-41.
7. Tang, Y.; Qi, S.; Zhu, L.; Zhuo, X.; Zhang, Y.; Meng, F. Obstacle avoidance motion in mobile robotics. J. Syst. Simul. 2024, 36, 1-26.
8. Wan, S.; Guan, S.; Tang, Y. Advancing bridge structural health monitoring: insights into knowledge-driven and data-driven approaches. J. Data. Sci. Intell. Syst. 2024, 2, 129-40.
9. Hu, K.; Chen, Z.; Kang, H.; Tang, Y. 3D vision technologies for a self-developed structural external crack damage recognition robot. Autom. Constr. 2024, 159, 105262.
10. Zou, C.; Mo, H.; Gao, C.; Du, R.; Fu, H. Language-based colorization of scene sketches. ACM. Trans. Graph. 2019, 38, 1-16.
11. Shin, Y. G.; Choi, K. A.; Kim, S. T.; Ko, S. J. A novel single IR light based gaze estimation method using virtual glints. IEEE. Trans. Consum. Electron. 2015, 61, 254-60.
12. Dong X, Li W, Wang X, Wang Y. Learning a deep convolutional network for colorization in monochrome-color dual-lens system. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019. pp. 8255–62.
13. Gupta RK, Chia AYS, Rajan D, Ng ES, Zhiyong H. Image colorization using similar images. In: Proceedings of the 20th ACM International Conference on Multimedia. MM '12. New York, NY, USA: Association for Computing Machinery; 2012. pp. 369–78.
14. Iizuka, S.; Simo-Serra, E.; Ishikawa, H. Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM. Trans. Graph. 2016, 35, 1-11.
15. Royer A, Kolesnikov A, Lampert CH. Probabilistic image colorization. arXiv 2017;arXiv: 1705.04258. Available from: https://doi.org/10.48550/arXiv.1705.04258. [accessed 8 Jan 2025].
16. Deshpande A, Lu J, Yeh MC, Jin Chong M, Forsyth D. Learning diverse image colorization. arXiv 2016;arXiv: 1612.01958. Available from: https://doi.org/10.48550/arXiv.1612.01958. [accessed 8 Jan 2025].
17. Larsson G, Maire M, Shakhnarovich G. Learning representations for automatic colorization. In: European conference on computer vision. Springer; 2016. pp. 577–93.
18. Levin A, Lischinski D, Weiss Y. Colorization using optimization. In: ACM SIGGRAPH 2004 Papers. SIGGRAPH '04. New York, NY, USA: Association for Computing Machinery; 2004. pp. 689–94.
19. Reinhard, E.; Adhikhmin, M.; Gooch, B.; Shirley, P. Color transfer between images. IEEE. Comput. Graph. Appl. 2001, 21, 34-41.
20. Larsson G, Maire M, Shakhnarovich G. Learning representations for automatic colorization. arXiv 2016;arXiv: 1603.06668. Available from: https://doi.org/10.48550/arXiv.1603.06668. [accessed 8 Jan 2025].
21. Zhao, J.; Han, J.; Shao, L.; Snoek, C. G. M. Pixelated semantic colorization. Int. J. Comput. Vis. 2020, 128, 818-34.
22. Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; et al. Generative adversarial networks. Commun. ACM. 2020, 63, 139-44.
23. Isola P, Zhu JY, Zhou T, Efros AA. Image-to-image translation with conditional adversarial networks. arXiv 2016;arXiv: 1611.07004. Available from: https://doi.org/10.48550/arXiv.1611.07004. [accessed 8 Jan 2025].
24. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. arXiv 2020;arXiv: 2006.11239. Available from: https://doi.org/10.48550/arXiv.2006.11239. [accessed 8 Jan 2025].
25. Song J, Meng C, Ermon S. Denoising diffusion implicit models. arXiv 2020;arXiv: 2010.02502. Available from: https://doi.org/10.48550/arXiv.2010.02502. [accessed 8 Jan 2025].
26. Saharia C, Chan W, Chang H, et al. Palette: image-to-image diffusion models. arXiv 2021;arXiv: 2111.05826. Available from: https://doi.org/10.48550/arXiv.2111.05826. [accessed 8 Jan 2025].
27. Berg A, Ahlberg J, Felsberg M. Generating visible spectrum images from thermal infrared. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018 Jun 18-22; Salt Lake City, USA. IEEE; 2018.
28. Kuang, X.; Zhu, J.; Sui, X.; et al. Thermal infrared colorization via conditional generative adversarial network. Infrared. Phys. Technol. 2020, 107, 103338.
29. Zhao, Y.; Po, L. M.; Cheung, K. W.; Yu, W. Y.; Rehman, Y. A. U. SCGAN: saliency map-guided colorization with generative adversarial network. IEEE. Trans. Circuits. Syst. Video. Technol. 2021, 31, 3062-77.
30. Liao, H.; Jiang, Q.; Jin, X.; et al. MUGAN: thermal infrared image colorization using mixed-skipping UNet and generative adversarial network. IEEE. Trans. Intell. Vehicles. 2023, 8, 2954-69.
31. He, Y.; Jin, X.; Jiang, Q.; et al. LKAT-GAN: a GAN for thermal infrared image colorization based on large kernel and attentionUNet-transformer. IEEE. Trans. Consum. Electron. 2023, 69, 478-89.
32. Luo, F.; Li, Y.; Zeng, G.; Peng, P.; Wang, G.; Li, Y. Thermal infrared image colorization for nighttime driving scenes with top-down guided attention. IEEE. Trans. Intell. Transp. Syst. 2022, 23, 15808-23.
33. Bansal A, Ma S, Ramanan D, Sheikh Y. Recycle-GAN: unsupervised video retargeting. arXiv 2018;arXiv: 1808.05174. Available from: https://doi.org/10.48550/arXiv.1808.05174. [accessed 8 Jan 2025].
34. Li S, Han B, Yu Z, Liu CH, Chen K, Wang S. I2V-GAN: unpaired infrared-to-visible video translation. arXiv 2021;arXiv: 2108.00913. Available from: https://doi.org/10.48550/arXiv.2108.00913. [accessed 8 Jan 2025].
35. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27-30; Las Vegas, USA. IEEE; 2016. pp. 770-8.
36. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. pp. 1–9. Available from: https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Szegedy_Going_Deeper_With_2015_CVPR_paper.html. [Last accessed on 8 Jan 2025].
37. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z. Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV); 2021 Oct 10-17; Montreal, Canada. IEEE; 2021. pp. 9992-10002.
38. Li, G.; Duan, N.; Fang, Y.; Gong, M.; Jiang, D. Unicoder-Vl: a universal encoder for vision and language by cross-modal pre-training. Proc. AAAI. Conf. Artif. Intell. 2020, 34, 11336-44.
39. Lu J, Batra D, Parikh D, Lee S. Vilbert: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv 2019;arXiv: 1908.02265. Available from: https://doi.org/10.48550/arXiv.1908.02265. [accessed 8 Jan 2025].
40. Tan H, Bansal M. Lxmert: learning cross-modality encoder representations from transformers. arXiv 2019;arXiv: 1908.07490. Available from: https://doi.org/10.48550/arXiv.1908.07490. [accessed 8 Jan 2025].
41. Radford A, Kim JW, Hallacy C, et al. Learning transferable visual models from natural language supervision. arXiv 2021;arXiv: 2103.00020. Available from: https://doi.org/10.48550/arXiv.2103.00020. [accessed 8 Jan 2025].
42. Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L. Segment anything. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 2023 Oct 01-06; Paris, France. IEEE; 2023. pp. 3992-4003.
43. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv 2014;arXiv: 1409.1556. Available from: https://doi.org/10.48550/arXiv.1409.1556. [accessed 8 Jan 2025].
44. Hwang S, Park J, Kim N, Choi Y, So Kweon I. Multispectral pedestrian detection: benchmark dataset and baseline. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2015 Jun 07-12; Boston, USA. IEEE; 2015. pp. 1037-45.
45. Luo, F. Y.; Liu, S. L.; Cao, Y. J.; Yang, K. F.; Xie, C. Y.; Liu, Y. Nighttime thermal infrared image colorization with feedback-based object appearance learning. IEEE. Trans. Circuits. Syst. Video. Technol. 2024, 34, 4745-61.
