Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
College of Optoelectronics, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China.
Sensors (Basel). 2023 Jan 17;23(3):1086. 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.
地基望远镜在获取空间图像时，通常会受到渐晕、杂散光和探测器非均匀性的影响。本文提出了一种基于条件生成对抗网络（）的空间图像非均匀性校正方法。首先，通过引入物理渐晕模型和设计模拟多项式来实现非均匀背景，创建了一个用于训练的数据集。其次，我们开发了一个鲁棒的条件生成对抗网络（）来学习非均匀背景，其中我们改进了生成器的网络结构。实验结果包括模拟数据集和真实的空间图像。所提出的方法可以有效地去除空间图像的非均匀背景，在模拟数据集中达到均方误差（）为 4.56，在真实图像校正中提高目标的信噪比（）43.87%。
