Variational Model-Based Deep Neural Networks for Image Reconstruction
Variational Model-Based Deep Neural Networks for Image Reconstruction
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DOI：
10.1007/978-3-030-03009-4_57-1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Yunmei Chen;X. Ye;Qingchao Zhang
中科院分区：
文献类型：
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作者：
Yunmei Chen;X. Ye;Qingchao Zhang
近年来，我们目睹了对图像重建的深度学习方法的研究兴趣空前增长。大多数这些方法的灵感来自发达的变分法和相关的优化算法的图像重建的逆问题。这些方法模仿了标准优化算法的迭代方案，但集成了可学习的组件以形成结构化的深度神经网络，并使用大量观察数据来训练网络以执行特定的重建任务。他们已经证明了显着改善的经验性能，并需要更低的计算成本相比，在各种应用中的经典方法。我们提供了详细的推导，网络架构，并在这个领域中的几个典型的网络的训练过程。
In recent years, we have witnessed unprecedented growth of research interests in deep learning approaches to image reconstruction. A majority of these approaches are inspired by the well-developed variational method and associated optimization algorithms for the inverse problem of image reconstruction. These approaches mimic the iterative schemes of the standard optimization algorithms but integrate learnable components to form structured deep neural networks and employ large amount of observation data to train the networks for the specific reconstruction tasks. They have demonstrated significantly improved empirical performance and require much lower computational cost compared to the classical methods in a variety of applications. We provide the details of the derivations, the network architectures, and the training procedures for several typical networks in this field.