In this paper, we propose an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks. As the human faces are highly structured and share unified facial components (e.g., eyes and mouths), such semantic information provides a strong prior for restoration. We incorporate face semantic labels as input priors and propose an adaptive structural loss to regularize facial local structures within an end-to-end deep convolutional neural network. Specifically, we first use a coarse deblurring network to reduce the motion blur on the input face image. We then adopt a parsing network to extract the semantic features from the coarse deblurred image. Finally, the fine deblurring network utilizes the semantic information to restore a clear face image. We train the network with perceptual and adversarial losses to generate photo-realistic results. The proposed method restores sharp images with more accurate facial features and details. Quantitative and qualitative evaluations demonstrate that the proposed face deblurring algorithm performs favorably against the state-of-the-art methods in terms of restoration quality, face recognition and execution speed.

中文翻译：

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利用语义进行人脸图像去模糊

在本文中，我们通过深度卷积神经网络利用语义线索提出了一种有效且高效的面部去模糊算法。由于人脸高度结构化并共享统一的面部组件（例如，眼睛和嘴巴），因此此类语义信息为恢复提供了强大的先验。我们将面部语义标签作为输入先验并提出自适应结构损失来规范端到端深度卷积神经网络中的面部局部结构。具体来说，我们首先使用粗去模糊网络来减少输入人脸图像上的运动模糊。然后我们采用解析网络从粗糙的去模糊图像中提取语义特征。最后，精细去模糊网络利用语义信息恢复清晰的人脸图像。我们用感知和对抗性损失训练网络以生成照片般逼真的结果。所提出的方法可以恢复具有更准确面部特征和细节的清晰图像。定量和定性评估表明，所提出的人脸去模糊算法在恢复质量、人脸识别和执行速度方面优于最先进的方法。