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:当面部图像模糊时,它会大大影响面部识别等高层次的视觉任务。面部图像模糊化的目的是从模糊的输入图像中恢复清晰的图像,这样可以提高识别准确度等等。然而,一般的面部模糊化方法在面部图像上效果不佳。因此,根据面部图像的特征,提出了一些面部模糊化方法,通过添加语体或结构信息作为具体的前缀来改进性能。在本文中,我们调查和总结最近公布的面部图像模糊化方法,其中大多基于深层学习。首先,我们简要介绍了图像模糊化的模型。接下来,我们将面部模糊化方法归纳为两类:基于模型的方法和基于深层学习的方法。此外,我们总结了神经网络培训过程中常用的数据集、损失功能和性能评估指标。我们展示了这些数据集和指标的经典方法的性能,并就基于模型的方法和基于学习的方法之间的差异进行简短的讨论。最后,我们讨论了当前的挑战和未来可能的研究方向。</s>