The increasing amount of space debris in recent years has greatly threatened space operation. In order to ensure the safety level of spacecraft, space debris perception via on-orbit visual sensors has become a promising solution. However, the perception capability of visual sensors largely depends on illumination, which tends to be insufficient in dark environments. Since the images captured by visible and infrared sensors are highly complementary in dark environments, a convolutional sparse representation-based visible and infrared image fusion algorithm is proposed in this paper to expand the applicability of visual sensors. In particular, the local contrast measure is applied to obtain the refined weight map for fusing the base layers, which is more robust in a dark space environment. The algorithm can settle two significant problems in space debris surveillance, namely, improving the signal-noise ratio in a noise space environment and preserving more detailed information in a dark space environment. A space debris dataset containing registered visible and infrared images has been purposely created and used for algorithm evaluation. Experimental results demonstrate that the proposed method in this paper is effective for enhancing image qualities and can achieve favorable effects compared to other state-of-the-art algorithms.

中文翻译：

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基于可见光和红外图像融合的图像质量增强与空间碎片在轨监视的应用

近年来，空间碎片的数量不断增加，极大地威胁着空间运行。为了保证航天器的安全水平，通过在轨视觉传感器感知空间碎片成为一种很有前景的解决方案。然而，视觉传感器的感知能力在很大程度上取决于光照，在黑暗环境中往往不足。由于可见光和红外传感器捕获的图像在黑暗环境中具有高度互补性，本文提出了一种基于卷积稀疏表示的可见光和红外图像融合算法，以扩展视觉传感器的适用性。特别是，应用局部对比度度量来获得用于融合基础层的细化权重图，这在暗空间环境中更加鲁棒。该算法可以解决空间碎片监视中的两个重要问题，即提高噪声空间环境中的信噪比和在暗空间环境中保留更详细的信息。特意创建了一个包含已注册可见光和红外图像的空间碎片数据集，并将其用于算法评估。实验结果表明，与其他最先进的算法相比，本文提出的方法在提高图像质量方面是有效的，并且可以达到良好的效果。