Details

Autor(en) / Beteiligte

• Guo, Xiangji
• Chen, Tao
• Liu, Junchi
• Liu, Yuan
• An, Qichang
• Jiang, Chunfeng

Titel

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

Ist Teil von

• Sensors (Basel, Switzerland), 2023-01, Vol.23 (3), p.1086

Ort / Verlag

Switzerland: MDPI AG

Erscheinungsjahr

2023

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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 ( ). 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 ( ) 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 ( ) of 4.56 in the simulation dataset, and improve the target's signal-to-noise ratio ( ) by 43.87% in the real image correction.