When the facial image is blurred, it has a great impact on high-level vision tasks such as face recognition. The purpose of facial image deblurring is to recover a clear image from a blurry input image, which can improve the recognition accuracy and so on. General deblurring methods can not perform well on facial images. So some face deblurring methods are proposed to improve the performance by adding semantic or structural information as specific priors according to the characteristics of facial images. This paper surveys and summarizes recently published methods for facial image deblurring, most of which are based on deep learning. Firstly, we give a brief introduction to the modeling of image blur. Next, we summarize face deblurring methods into two categories, namely model-based methods and deep learning-based methods. Furthermore, we summarize the datasets, loss functions, and performance evaluation metrics commonly used in the neural network training process. We show the performance of classical methods on these datasets and metrics and give a brief discussion on the differences of model-based and learning-based methods. Finally, we discuss current challenges and possible future research directions.

翻译：当人脸图像模糊时，会对人脸识别等高级视觉任务产生重大影响。人脸图像去模糊旨在从模糊输入图像中恢复清晰图像，从而提升识别精度等性能。通用去模糊方法在人脸图像上表现不佳。因此，一些人脸去模糊方法被提出，通过根据人脸图像特性添加语义或结构信息作为特定先验来改进性能。本文调研并总结了近期发表的人脸图像去模糊方法，其中多数基于深度学习。首先，我们简要介绍了图像模糊建模。其次，我们将人脸去模糊方法归纳为两类，即基于模型的方法和基于深度学习的方法。此外，我们总结了神经网络训练过程中常用的数据集、损失函数和性能评估指标。我们展示了经典方法在这些数据集和指标上的性能，并简要讨论了基于模型方法与基于学习方法之间的差异。最后，我们探讨了当前挑战及可能的未来研究方向。