Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network

doi:10.3390/s23031086

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	Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network
	X. Guo; T. Chen; J. Liu; Y. Liu; Q. An and C. Jiang
刊名	Sensors
	2023
卷号	23 期号:3
ISSN号	14248220
DOI	10.3390/s23031086
英文摘要	Ground-based telescopes are often affected by vignetting, stray light and detector nonuniformity when acquiring space images. This paper presents a space image nonuniform correction method using the conditional generative adversarial network (CGAN). Firstly, we create a dataset for training by introducing the physical vignetting model and by designing the simulation polynomial to realize the nonuniform background. Secondly, we develop a robust conditional generative adversarial network (CGAN) for learning the nonuniform background, in which we improve the network structure of the generator. The experimental results include a simulated dataset and authentic space images. The proposed method can effectively remove the nonuniform background of space images, achieve the Mean Square Error (MSE) of 4.56 in the simulation dataset, and improve the target’s signal-to-noise ratio (SNR) by 43.87% in the real image correction. © 2023 by the authors.
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内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/67498]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	X. Guo,T. Chen,J. Liu,et al. Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network[J]. Sensors,2023,23(3).
APA	X. Guo,T. Chen,J. Liu,Y. Liu,&Q. An and C. Jiang.(2023).Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network.Sensors,23(3).
MLA	X. Guo,et al."Nonuniform Correction of Ground-Based Optical Telescope Image Based on Conditional Generative Adversarial Network".Sensors 23.3(2023).