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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 3; p. 1086
• Main Authors: Guo, Xiangji, Chen, Tao, Liu, Junchi, Liu, Yuan, An, Qichang, Jiang, Chunfeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17-01-2023; MDPI
• Subjects: Algorithms; Artificial intelligence; conditional generative adversarial network; Datasets; Deep learning; Image acquisition; Image processing; Light; Methods; Neural networks; nonuniform correction; Nonuniformity; optical telescope image; Polynomials; Radiation; Regularization methods; Sensors; Signal to noise ratio; Simulation; Technology application; Telescope; Telescopes; Vignetting; China; deep learning; conditional generative adversarial network; nonuniform correction; optical telescope image
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23031086

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.