  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.
