We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.

中文翻译：

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通过结构生成和细节增强实现关节面幻觉和去模糊

我们解决了从模糊的低分辨率输入恢复高分辨率人脸图像的问题。这个问题很困难，因为超分辨率和去模糊需要同时解决。此外，现有算法不能很好地处理人脸图像，因为低分辨率人脸图像没有太多纹理，这对于去模糊尤其重要。在本文中，我们提出了一种有效的算法，利用人脸的特定领域知识来恢复高质量的人脸。我们首先提出了一种面部成分引导的深度卷积神经网络 (CNN) 来恢复粗糙的面部图像，该图像表示为基础图像，其中面部成分是从输入面部图像自动生成的。然而，基于 CNN 的方法不能很好地处理图像细节。我们通过面部组件匹配进一步开发了一种新的基于样本的细节增强算法。大量实验表明，所提出的方法在数量和质量上都优于最先进的算法。