论文标题
具有线性深度图像先验的计算机断层扫描的不确定性估计
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
论文作者
论文摘要
现有的基于深度学习的层析成像图像重建方法不能准确估计重建不确定性，从而阻碍了其现实世界的部署。本文开发了一种方法，称为线性深度图像先验（DIP），以估计与DIP产生的重建和总变化正则化（TV）相关的不确定性。具体而言，我们将DIP赋予从其优化参数围绕神经网络的局部线性化计算的共轭高斯线性模型类型误差线。为了保留共轭，我们用高斯代理人近似电视台。这种方法可提供像素的不确定性估计值和超参数优化的边际可能性目标。我们演示了有关合成数据和实验室高分辨率2D $μ$ CT数据的方法，并表明它提供了相对于先前浸出的概率公式的不确定性估计值的卓越校准。我们的代码可在https://github.com/educating-dip/bayes_dip上找到。
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment. This paper develops a method, termed as the linearised deep image prior (DIP), to estimate the uncertainty associated with reconstructions produced by the DIP with total variation regularisation (TV). Specifically, we endow the DIP with conjugate Gaussian-linear model type error-bars computed from a local linearisation of the neural network around its optimised parameters. To preserve conjugacy, we approximate the TV regulariser with a Gaussian surrogate. This approach provides pixel-wise uncertainty estimates and a marginal likelihood objective for hyperparameter optimisation. We demonstrate the method on synthetic data and real-measured high-resolution 2D $μ$CT data, and show that it provides superior calibration of uncertainty estimates relative to previous probabilistic formulations of the DIP. Our code is available at https://github.com/educating-dip/bayes_dip.
