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arXiv 2403.18052astro-ph.IMcs.LGeess.IVeess.SP
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R2D2射电天文图像重建及模型不确定性量化

R2D2 image reconstruction with model uncertainty quantification in radio astronomy

Institute of Sensors, Signals and Systems, Heriot-Watt University（赫瑞瓦特大学传感器、信号与系统研究所）

Amir Aghabiglou, Chung San Chu, Arwa Dabbech, Yves Wiaux

备注：
Accepted to IEEE EUSIPCO 2024
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AI总结：

本研究评估R2D2射电干涉成像算法的稳健性，通过集成多实例生成样本以量化模型不确定性，实验表明其重建能力优于现有算法且不确定性极低。

摘要：

“用于高动态范围成像的残差到残差DNN系列”（R2D2）方法最近被引入射电干涉（RI）成像领域。R2D2的重建由一系列残差图像构成，这些残差图像由深度神经网络（DNN）迭代估计得出，每个DNN以上一次迭代的图像估计和相应的数据残差作为输入。在本工作中，我们通过研究其一系列学习模型相关的不确定性，来考察R2D2图像估计过程的稳健性。采用集成平均方法，可以训练多个系列，这些系列源于每次迭代训练过程中不同的随机DNN初始化。由此产生的多个R2D2实例也可用于生成“R2D2样本”，从中得到的经验均值和标准差使该算法具备联合估计与不确定性量化功能。聚焦于RI成像，并采用针对特定望远镜的方法，我们训练了多个R2D2实例，以涵盖甚大阵列（VLA）最一般的观测设置。模拟和真实数据实验证实：（i）R2D2的图像估计能力优于最先进的算法；（ii）其超快速重建能力（源于仅包含少量DNN的系列）使得即使在较大图像尺寸下，计算多个重建样本和不确定性图也变得切实可行；（iii）其特点是模型不确定性非常低。

英文摘要：

The ``Residual-to-Residual DNN series for high-Dynamic range imaging'' (R2D2) approach was recently introduced for Radio-Interferometric (RI) imaging in astronomy. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of Deep Neural Networks (DNNs) taking the previous iteration's image estimate and associated data residual as inputs. In this work, we investigate the robustness of the R2D2 image estimation process, by studying the uncertainty associated with its series of learned models. Adopting an ensemble averaging approach, multiple series can be trained, arising from different random DNN initializations of the training process at each iteration. The resulting multiple R2D2 instances can also be leveraged to generate ``R2D2 samples'', from which empirical mean and standard deviation endow the algorithm with a joint estimation and uncertainty quantification functionality. Focusing on RI imaging, and adopting a telescope-specific approach, multiple R2D2 instances were trained to encompass the most general observation setting of the Very Large Array (VLA). Simulations and real-data experiments confirm that: (i) R2D2's image estimation capability is superior to that of the state-of-the-art algorithms; (ii) its ultra-fast reconstruction capability (arising from series with only few DNNs) makes the computation of multiple reconstruction samples and of uncertainty maps practical even at large image dimension; (iii) it is characterized by a very low model uncertainty.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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