Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.

翻译：射电望远镜生成关于天体的可见度数据，但这些数据具有稀疏性和噪声性。因此，基于原始可见度数据生成的图像质量较低。近期研究采用深度学习模型重建可见度数据以获得更清晰的图像。然而，这些方法依赖大量标记训练数据，需要射电天文学家付出显著的标记工作。针对这一问题，我们提出VisRec——一种与模型无关的半监督学习方法，用于可见度数据重建。具体而言，VisRec由监督学习模块和无监督学习模块组成。在监督学习模块中，我们引入一组数据增强函数以生成多样化的训练样本；相比之下，VisRec的无监督学习模块对未标记数据进行增强，并将非增强可见度数据的重建结果作为伪标签用于训练。这种混合方法使VisRec能够有效利用标记和未标记数据。通过这种方式，即使在标记数据稀缺的情况下，VisRec仍表现优异。评估结果表明，VisRec在重建质量、对常见观测扰动的鲁棒性以及不同望远镜配置下的泛化能力方面均优于所有基线方法。