In radio astronomy, signals from radio telescopes are transformed into images of observed celestial objects, or sources. However, these images, called dirty images, contain real sources as well as artifacts due to signal sparsity and other factors. Therefore, radio interferometric image reconstruction is performed on dirty images, aiming to produce clean images in which artifacts are reduced and real sources are recovered. So far, existing methods have limited success on recovering faint sources, preserving detailed structures, and eliminating artifacts. In this paper, we present VIC-DDPM, a Visibility and Image Conditioned Denoising Diffusion Probabilistic Model. Our main idea is to use both the original visibility data in the spectral domain and dirty images in the spatial domain to guide the image generation process with DDPM. This way, we can leverage DDPM to generate fine details and eliminate noise, while utilizing visibility data to separate signals from noise and retaining spatial information in dirty images. We have conducted experiments in comparison with both traditional methods and recent deep learning based approaches. Our results show that our method significantly improves the resulting images by reducing artifacts, preserving fine details, and recovering dim sources. This advancement further facilitates radio astronomical data analysis tasks on celestial phenomena.

翻译：在射电天文学中，来自射电望远镜的信号被转化为观测天体（即源）的图像。然而，这些称为脏图的图像，除了真实源之外，还包含因信号稀疏性等因素产生的伪影。因此，需对脏图执行射电干涉图像重建，旨在生成伪影减少且真实源得以恢复的干净图像。迄今为止，现有方法在恢复微弱源、保留精细结构及消除伪影方面成效有限。本文提出VIC-DDPM——一种可见度与图像条件去噪扩散概率模型。我们的核心思想是同时利用频谱域中的原始可见度数据与空间域中的脏图，以DDPM引导图像生成过程。如此，可借助DDPM生成精细细节并消除噪声，同时利用可见度数据将信号与噪声分离，并保留脏图中的空间信息。我们开展了与传统方法及近期基于深度学习的对比实验。结果表明，我们的方法通过减少伪影、保留精细细节及恢复暗弱源，显著提升了最终图像质量。这一进展进一步促进了射电天文学中关于天体现象的数据分析任务。