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.

本文介绍了一种名为VIC-DDPM的可见度和图像调节的去噪扩散概率模型，它能通过在频谱域中使用原始可见度数据和在空间域中使用dirty images来引导图像生成过程，并在保留噪声中的信号并恢复dirty images中的空间信息的同时利用DDPM生成细节和消除噪声。 实验结果表明，VIC-DDPM显着改善了结果图像，减少了伪影，保留了细节，恢复了暗源，进一步促进了对天文现象的射电天文学数据分析任务。

一种基于条件去噪扩散概率模型的射电干涉成像重构方法