Compensation of temperature effects on imaging quality of thermal imaging objectives using deep learning techniques
74 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.99.2024.89-98Keywords:
Thermal imaging objectives; Temperature variation compensation; Deep learning technique.Abstract
Thermal imaging objectives are made from infrared materials with large thermal expansion coefficients, such as Ge, Si, and ZnSe. When the temperature changes, it leads to variations in the refractive index, curvature radius, and thickness of the lens, causing defocus shifts that degrade the image quality of the thermal imaging system. In this paper, we propose a novel method to compensate for the effects of temperature variations on the quality of thermal imaging objectives by using deep learning techniques. The temperature variations are measured using a thermal sensor. Subsequently, a U-Net network is employed to mitigate the impact of temperature on the imaging quality of the thermal imaging objectives without requiring any optical displacement or replacement of the lens. Simulation results show that the proposed method performs the effectively compensation for the influence of temperature changes on thermal imaging objective over a wide temperature range from -5 °C to 50 °C.
References
[1]. Lee Y W, “Three-shell-based lens barrel for the effective athermalization of an IR optical system,” Appl. Opt. 50 6206–13, (2011). DOI: https://doi.org/10.1364/AO.50.006206
[2]. Feng B, Shi Z, Xu B, Zhang C and Zhang X, “ZnSe material phase mask applied to athermalization of infrared imaging system,” Appl. Opt. 55 5715–20, (2016). DOI: https://doi.org/10.1364/AO.55.005715
[3]. Xie H, Su Y, Zhu M, Yang L, Wang S, Wang X and Yang T, “Athermalization of infrared optical system through wavefront coding,” Opt. Commun. 441 106–12, (2019). DOI: https://doi.org/10.1016/j.optcom.2019.02.043
[4]. Feng B, Shi Z, Chang Z, Liu H and Zhao Y, “110 °C range athermalization of wavefront coding infrared imaging systems,” Infrared Phys. Technol. 85 157–62, (2017). DOI: https://doi.org/10.1016/j.infrared.2017.05.020
[5]. Feng B, Shi Z, Zhao Y, Liu H and Liu LA, “Wide-FoV athermalized infrared imaging system with a two-element lens,” Infrared Phys. Technol. 87 11–21, (2017). DOI: https://doi.org/10.1016/j.infrared.2017.09.021
[6]. Philip J. R "Athermalization of IR optical systems," Proc. SPIE 10260, Infrared Optical Design and Fabrication: A Critical Review, 102600F, (1991). DOI: https://doi.org/10.1117/12.48455
[7]. Yu-qing H, Jia-qi L, Jing P, and Ying-jiao L, "Optimization of phase mask-based iris imaging system through the optical characteristics," Proc. SPIE 8711, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII, 871107, (2013). DOI: https://doi.org/10.1117/12.2015790
[8]. Gunther K "Automatic active athermalization of infrared optical systems," Proc. SPIE 1540, Infrared Technology XVII, (1991). DOI: https://doi.org/10.1117/12.48768
[9]. Hongda W, Yair R, “Deep learning enables cross-modality super-resolution in fluorescence microscopy,” Nature Methods, (2019).
[10]. Rong C, Xiao T, “Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging,” Nat Commun, (2023).