MRI的期望一致即插即用
Expectation Consistent Plug-and-Play for MRI
摘要：
对于图像恢复问题，已经开发了即插即用（Plug-and-Play，PPENS）方法，其用对通常使用深度神经网络实现的专用去噪器的调用来替换优化算法中的邻近步骤。虽然这些方法是成功的，但它们可以改进。例如，去噪器通常使用高斯白噪声进行训练，而Pennsylvania的去噪器输入误差通常远离白色和高斯，并且很难从迭代到迭代预测统计数据。基于近似消息传递（AMP）的Pendant方法是一个例外，但只有当前向运算符表现得像一个大的随机矩阵时。在这项工作中，我们使用期望一致（EC）近似算法（AMP的推广）设计了一种Pestrian方法，该算法在每次迭代时提供可预测的误差统计，可以有效地训练深度网络去噪器。
英文摘要：
For image recovery problems, plug-and-play (PnP) methods have been developed that replace the proximal step in an optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network. Although such methods have been successful, they can be improved. For example, the denoiser is often trained using white Gaussian noise, while PnP's denoiser input error is often far from white and Gaussian, with statistics that are difficult to predict from iteration to iteration. PnP methods based on approximate message passing (AMP) are an exception, but only when the forward operator behaves like a large random matrix. In this work, we design a PnP method using the expectation consistent (EC) approximation algorithm, a generalization of AMP, that offers predictable error statistics at each iteration, from which a deep-net denoiser can be effectively trained.
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