When a facial image is blurred, it significantly affects 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, etc. However, general deblurring methods do not perform well on facial images. Therefore, some face deblurring methods have been proposed to improve performance by adding semantic or structural information as specific priors according to the characteristics of the facial images. In this paper, we survey and summarize recently published methods for facial image deblurring, most of which are based on deep learning. First, we provide a brief introduction to the modeling of image blurring. Next, we summarize face deblurring methods into two categories: 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 provide a brief discussion on the differences between model-based and learning-based methods. Finally, we discuss the current challenges and possible future research directions.

翻译：当人脸图像模糊时，会显著影响人脸识别等高层视觉任务。人脸图像去模糊的目标是从模糊输入图像中恢复清晰图像，从而提高识别准确率等。然而，通用去模糊方法在人脸图像上表现不佳。因此，一些针对人脸去模糊的方法被提出，通过根据人脸图像的特定属性添加语义或结构信息作为特定先验来提高性能。本文综述并总结了近年来发表的人脸图像去模糊方法，其中大多基于深度学习。首先，我们简要介绍了图像模糊建模。接着，我们将人脸去模糊方法归纳为两类：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与基于学习方法之间的差异。最后，我们探讨了当前面临的挑战及未来可能的研究方向。