论文标题
模型适应成像中的反问题
Model Adaptation for Inverse Problems in Imaging
论文作者
论文摘要
深层神经网络已成功应用于计算成像中引起的各种反问题。这些网络通常是使用向前模型训练的，该模型描述要倒置的测量过程，该过程通常直接将其直接纳入网络本身。但是，这些方法对正向模型的变化很敏感：如果在测试时，正向模型（甚至略有）与网络的训练相反（甚至略有不同），则重建性能会大大降低。给定一个通过已知的远期模型来解决初始反向问题的网络，我们提出了两个新型程序，即使在不完全了解变化的情况下，也可以使网络适应向前模型的变化。我们的方法不需要访问更标记的数据（即地面真相图像）。我们显示了这些简单的模型适应方法在各种反问题中取得了经验成功，包括磁共振成像中的脱张，超分辨率和不足采样的图像重建。
Deep neural networks have been applied successfully to a wide variety of inverse problems arising in computational imaging. These networks are typically trained using a forward model that describes the measurement process to be inverted, which is often incorporated directly into the network itself. However, these approaches are sensitive to changes in the forward model: if at test time the forward model varies (even slightly) from the one the network was trained for, the reconstruction performance can degrade substantially. Given a network trained to solve an initial inverse problem with a known forward model, we propose two novel procedures that adapt the network to a change in the forward model, even without full knowledge of the change. Our approaches do not require access to more labeled data (i.e., ground truth images). We show these simple model adaptation approaches achieve empirical success in a variety of inverse problems, including deblurring, super-resolution, and undersampled image reconstruction in magnetic resonance imaging.
