We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards challenging regions of the human face such as the eyes and nose. The entire network is trained in an end-to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at: https://github.com/rajeevyasarla/UMSN-Face-Deblurring

该文章提出了一种新的多流架构和训练方法，利用语义标签进行面部图像去模糊。本文提出的不确定性引导多流语义网络（UMSN）独立处理属于每个语义类别的区域，并学习将它们的输出组合成最终的去模糊结果。此外，在训练期间，使用预测的置信度度量来引导网络进入人脸的挑战性区域。与现有最先进的面部去模糊方法相比，综合三个不同的面部数据集的实验证明，所提出的方法取得了显著的改进。

使用不确定性引导的多流语义网络去模糊面部图像