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
	2023
发表期刊	Sensors
ISSN	14248220
卷号	23 期号:3
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.
DOI	10.3390/s23031086
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收录类别	sci ; ei
引用统计
文献类型	期刊论文
条目标识符	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).

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