论文标题
wppnets and wppflows：瓦斯尔斯坦补丁先验的力量
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
论文作者
论文摘要
利用图像补丁而不是整个图像已被证明是解决图像处理中各种问题的强大方法。最近，基于未知图像和参考图像的斑块分布的比较，Wasserstein Patch Priors（WPP）成功地用作数据驱动的正则化合物，用于超级分辨率的变异表格。但是，对于每个输入图像，此方法需要解决一个非凸极小问题的解决方案，该问题在计算上是昂贵的。在本文中，我们建议根据WPP损失功能以无监督的方式学习两种神经网络。首先，我们展示了如何可以合并卷积神经网络（CNN）。一旦学习了称为WPPNET的网络，就可以非常有效地应用于任何输入图像。其次，我们结合了有条件的归一化流，以提供不确定性定量的工具。数值示例证明了WPPNET在各种图像类中的上分辨率的表现非常好，即使仅知道向前操作员。
Exploiting image patches instead of whole images have proved to be a powerful approach to tackle various problems in image processing. Recently, Wasserstein patch priors (WPP), which are based on the comparison of the patch distributions of the unknown image and a reference image, were successfully used as data-driven regularizers in the variational formulation of superresolution. However, for each input image, this approach requires the solution of a non-convex minimization problem which is computationally costly. In this paper, we propose to learn two kind of neural networks in an unsupervised way based on WPP loss functions. First, we show how convolutional neural networks (CNNs) can be incorporated. Once the network, called WPPNet, is learned, it can be very efficiently applied to any input image. Second, we incorporate conditional normalizing flows to provide a tool for uncertainty quantification. Numerical examples demonstrate the very good performance of WPPNets for superresolution in various image classes even if the forward operator is known only approximately.
