The volume of space debris currently orbiting the Earth is reaching an unsustainable level at an accelerated pace. The detection, tracking, identification, and differentiation between orbit-defined, registered spacecraft, and rogue/inactive space ``objects'', is critical to asset protection. The primary objective of this work is to investigate the validity of Deep Neural Network (DNN) solutions to overcome the limitations and image artefacts most prevalent when captured with monocular cameras in the visible light spectrum. In this work, a hybrid UNet-ResNet34 Deep Learning (DL) architecture pre-trained on the ImageNet dataset, is developed. Image degradations addressed include blurring, exposure issues, poor contrast, and noise. The shortage of space-generated data suitable for supervised DL is also addressed. A visual comparison between the URes34P model developed in this work and the existing state of the art in deep learning image enhancement methods, relevant to images captured in space, is presented. Based upon visual inspection, it is determined that our UNet model is capable of correcting for space-related image degradations and merits further investigation to reduce its computational complexity.

翻译：当前环绕地球运行的太空碎片体积正以加速趋势达到不可持续的水平。对轨道定义、已登记航天器与失控/非活跃太空“物体”的探测、跟踪、识别与区分，对于资产保护至关重要。本研究的主要目标是验证深度神经网络（DNN）解决方案在克服可见光谱单目相机捕获图像时最常见局限性与伪影方面的有效性。本研究开发了一种基于ImageNet数据集预训练的混合UNet-ResNet34深度学习（DL）架构。解决的图像退化问题包括模糊、曝光问题、对比度差及噪声。同时解决了适用于监督学习的太空生成数据短缺的问题。本文对所开发的URes34P模型与现有的、适用于太空图像捕获的深度学习图像增强方法进行了视觉对比。基于视觉评估，确定我们的UNet模型能够校正太空相关图像退化，并值得进一步研究以降低其计算复杂度。