WPPNets与WPPFlows：Wasserstein图像块先验在超分辨率中的威力
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution
AI总结：
本文提出基于Wasserstein图像块先验损失无监督学习WPPNet和条件归一化流WPPFlows，以高效替代传统非凸优化并支持不确定性量化，在超分辨率任务中取得优异性能。
摘要：
利用图像块而非整幅图像已被证明是解决图像处理中各种问题的有力方法。最近，基于比较未知图像与参考图像的图像块分布的Wasserstein图像块先验（WPP）被成功用作超分辨率变分公式中的数据驱动正则化器。然而，对于每个输入图像，这种方法都需要求解一个非凸最小化问题，计算代价很高。在本文中，我们提出基于WPP损失函数以无监督方式学习两类神经网络。首先，我们展示了如何引入卷积神经网络（CNN）。一旦学习了称为WPPNet的网络，它就可以非常高效地应用于任何输入图像。其次，我们引入条件归一化流，为不确定性量化提供工具。数值实验表明，即使前向算子仅被近似已知，WPPNets在多种图像类别上的超分辨率性能也非常出色。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
