  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.
