论文标题
SUREMAP：使用Stein的无偏风险估计预测基于CNN的图像重建的不确定性
SUREMap: Predicting Uncertainty in CNN-based Image Reconstruction Using Stein's Unbiased Risk Estimate
论文作者
论文摘要
卷积神经网络（CNN）已成为解决计算成像重建问题的强大工具。但是，CNN通常很难理解黑盒。因此，知道何时工作以及更重要的是，他们何时失败是一个挑战。这种限制是它们在安全性应用中使用（例如医学成像）中使用的主要障碍：重建是人工制品还是肿瘤中的斑点？ 在这项工作中，我们使用Stein的无偏风险估计（当然）以热图的形式开发每像素置信区间，以使用基于CNN的Deoisers的近似消息传递（AMP）框架进行压缩感测重建。这些热图告诉最终用户可以信任由CNN形成的图像，这可以大大改善CNN在各种计算成像应用中的实用性。
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This limitation is a major barrier to their use in safety-critical applications like medical imaging: Is that blob in the reconstruction an artifact or a tumor? In this work we use Stein's unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. These heatmaps tell end-users how much to trust an image formed by a CNN, which could greatly improve the utility of CNNs in various computational imaging applications.
