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:当面部图像模糊时,它就会对面部图像识别等高层次的视觉任务产生巨大影响。面部图像分流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。一般的分流方法无法在面部图像上很好地发挥作用。因此,提出了一些面部分流方法,以便根据面部图像的特征作为具体的前缀添加语义或结构信息,从而改进性能。本文的纸张调查并概述了最近公布的面部图像分流方法,其中大多基于深层次的学习。首先,我们简要介绍了图像的模型模糊性。接下来,我们将面临分流方法分为两类,即基于模型的方法和基于深层次的学习方法。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。我们在这些数据集和指标上展示了经典方法的性能,并简要讨论了基于模型和学习方法的差异。最后,我们讨论了当前的挑战和可能的未来研究方向。