We explore the use of deep learning to localise galactic structures in low surface brightness (LSB) images. LSB imaging reveals many interesting structures, though these are frequently confused with galactic dust contamination, due to a strong local visual similarity. We propose a novel unified approach to multi-class segmentation of galactic structures and of extended amorphous image contaminants. Our panoptic segmentation model combines Mask R-CNN with a contaminant specialised network and utilises an adaptive preprocessing layer to better capture the subtle features of LSB images. Further, a human-in-the-loop training scheme is employed to augment ground truth labels. These different approaches are evaluated in turn, and together greatly improve the detection of both galactic structures and contaminants in LSB images.

翻译：本文探讨了利用深度学习在低表面亮度（LSB）图像中定位星系结构的方法。LSB成像揭示了诸多有趣的结构，但由于强烈的局部视觉相似性，这些结构常与星系尘埃污染相混淆。我们提出了一种新颖的统一方法，用于对星系结构和扩展的无定形图像污染物进行多类别分割。我们的全景分割模型将Mask R-CNN与污染物专用网络相结合，并利用自适应预处理层以更好地捕捉LSB图像的细微特征。此外，采用人机协同训练方案来增强真实标签。这些不同方法依次进行评估，共同显著提升了LSB图像中星系结构和污染物的检测效果。