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.

中文翻译：

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成像中逆问题的模型适应

深度神经网络已成功应用于计算成像中出现的各种逆问题。这些网络通常使用前向模型进行训练，该模型描述了要反转的测量过程，该模型通常直接合并到网络本身中。然而，这些方法对前向模型的变化很敏感：如果在测试时前向模型与训练网络的模型不同（甚至轻微），重建性能可能会大大降低。给定一个经过训练以解决具有已知前向模型的初始逆问题的网络，我们提出了两种新颖的程序，即使没有完全了解变化，也可以使网络适应前向模型的变化。我们的方法不需要访问更多标记数据（即地面实况图像）。