Zhang Haopeng, Wang Pengrui, Zhang Cong, Jiang Zhiguo
Image Processing Center, School of Astronautics, Beihang University, Beijing 102206, China.
Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 102206, China.
Sensors (Basel). 2019 Jul 23;19(14):3234. doi: 10.3390/s19143234.
In the case of space-based space surveillance (SBSS), images of the target space objects captured by space-based imaging sensors usually suffer from low spatial resolution due to the extremely long distance between the target and the imaging sensor. Image super-resolution is an effective data processing operation to get informative high resolution images. In this paper, we comparably study four recent popular models for single image super-resolution based on convolutional neural networks (CNNs) with the purpose of space applications. We specially fine-tune the super-resolution models designed for natural images using simulated images of space objects, and test the performance of different CNN-based models in different conditions that are mainly considered for SBSS. Experimental results show the advantages and drawbacks of these models, which could be helpful for the choice of proper CNN-based super-resolution method to deal with image data of space objects.
在天基空间监视（SBSS）的情况下，由于目标与成像传感器之间的距离极远，天基成像传感器捕获的目标空间物体图像通常空间分辨率较低。图像超分辨率是一种有效的数据处理操作，用于获取信息丰富的高分辨率图像。在本文中，我们为了空间应用目的，对四种基于卷积神经网络（CNN）的单图像超分辨率的最新流行模型进行了比较研究。我们使用空间物体的模拟图像对为自然图像设计的超分辨率模型进行了专门的微调，并在主要针对SBSS考虑的不同条件下测试了不同基于CNN的模型的性能。实验结果显示了这些模型的优缺点，这有助于选择合适的基于CNN的超分辨率方法来处理空间物体的图像数据。
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