论文标题
基于深度展开的计算成像的不确定性量化
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
论文作者
论文摘要
深度展开是一种基于深度学习的图像重建方法，它弥合了基于模型的基于模型和纯粹的基于深度学习的图像重建方法之间的差距。尽管深度展开的方法实现了成像问题的最新性能，并允许将观察模型纳入重建过程，但它们并未提供有关重建图像的任何不确定性信息，这严重限制了其在实践中的使用，尤其是用于安全临界成像应用。在本文中，我们提出了一个基于学习的图像重建框架，该框架将观察模型纳入重建任务中，并且能够基于深度展开和贝叶斯神经网络来量化认知和态度不确定性。我们证明了所提出的框架在磁共振成像和计算机断层扫描重建问题上的不确定性表征能力。我们调查了拟议框架提供的认知和态度不确定性信息的特征，以激发未来的研究利用不确定性信息来开发更准确，健壮，可信赖，不确定性意识，基于学习的图像重建和成像问题的分析方法。我们表明，所提出的框架可以提供不确定性信息，同时与最先进的深层展开方法实现可比的重建性能。
Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve state-of-the-art performance for imaging problems and allow the incorporation of the observation model into the reconstruction process, they do not provide any uncertainty information about the reconstructed image, which severely limits their use in practice, especially for safety-critical imaging applications. In this paper, we propose a learning-based image reconstruction framework that incorporates the observation model into the reconstruction task and that is capable of quantifying epistemic and aleatoric uncertainties, based on deep unrolling and Bayesian neural networks. We demonstrate the uncertainty characterization capability of the proposed framework on magnetic resonance imaging and computed tomography reconstruction problems. We investigate the characteristics of the epistemic and aleatoric uncertainty information provided by the proposed framework to motivate future research on utilizing uncertainty information to develop more accurate, robust, trustworthy, uncertainty-aware, learning-based image reconstruction and analysis methods for imaging problems. We show that the proposed framework can provide uncertainty information while achieving comparable reconstruction performance to state-of-the-art deep unrolling methods.
