Reconstructing sky models from dirty radio images for accurate source
localization and flux estimation is crucial for studying galaxy evolution at
high redshift, especially in deep fields using instruments like the Atacama
Large Millimetre Array (ALMA). With new projects like the Square Kilometre
Array (SKA), there's a growing need for better source extraction methods.
Current techniques, such as CLEAN and PyBDSF, often fail to detect faint
sources, highlighting the need for more accurate methods. This study proposes
using stochastic neural networks to rebuild sky models directly from dirty
images. This method can pinpoint radio sources and measure their fluxes with
related uncertainties, marking a potential improvement in radio source
characterization. We tested this approach on 10164 images simulated with the
CASA tool simalma, based on ALMA's Cycle 5.3 antenna setup. We applied
conditional Denoising Diffusion Probabilistic Models (DDPMs) for sky models
reconstruction, then used Photutils to determine source coordinates and fluxes,
assessing the model's performance across different water vapor levels. Our
method showed excellence in source localization, achieving more than 90%
completeness at a signal-to-noise ratio (SNR) as low as 2. It also surpassed
PyBDSF in flux estimation, accurately identifying fluxes for 96% of sources in
the test set, a significant improvement over CLEAN+ PyBDSF's 57%. Conditional
DDPMs is a powerful tool for image-to-image translation, yielding accurate and
robust characterisation of radio sources, and outperforming existing
methodologies. While this study underscores its significant potential for
applications in radio astronomy, we also acknowledge certain limitations that
accompany its usage, suggesting directions for further refinement and research.

使用随机神经网络从粗糙的无线电图像中重建天空模型，实现准确的源定位和通量估计，为高红移下研究星系演化提供关键支持。

基于条件去噪扩散模型的射电天文图像重构

Radio-astronomical Image Reconstruction with Conditional Denoising  Diffusion Model

Performing magnetic resonance imaging (MRI) reconstruction from under-sampled
k-space data can accelerate the procedure to acquire MRI scans and reduce
patients' discomfort. The reconstruction problem is usually formulated as a
denoising task that removes the noise in under-sampled MRI image slices.
Although previous GAN-based methods have achieved good performance in image
denoising, they are difficult to train and require careful tuning of
hyperparameters. In this paper, we propose a novel MRI denoising framework
DiffCMR by leveraging conditional denoising diffusion probabilistic models.
Specifically, DiffCMR perceives conditioning signals from the under-sampled MRI
image slice and generates its corresponding fully-sampled MRI image slice.
During inference, we adopt a multi-round ensembling strategy to stabilize the
performance. We validate DiffCMR with cine reconstruction and T1/T2 mapping
tasks on MICCAI 2023 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset.
Results show that our method achieves state-of-the-art performance, exceeding
previous methods by a significant margin. Code is available at
this https URL

本研究提出了一种基于条件去噪扩散概率模型的新型磁共振成像 (MRI) 图像去噪框架 DiffCMR，通过感知来自欠采样 MRI 图像切片的条件信号，并生成相应的全采样 MRI 图像切片。经过多轮合成策略的推断，验证了 DiffCMR 在 MICCAI 2023 心脏 MRI 重建挑战数据集上的心动影像重建和 T1/T2 映射任务，并显示出超越先前方法的最先进性能。