A survey on facial image deblurring
- URL: http://arxiv.org/abs/2302.05017v1
- Date: Fri, 10 Feb 2023 02:24:56 GMT
- Title: A survey on facial image deblurring
- Authors: Bingnan Wang, Fanjiang Xu and Quan Zheng
- Abstract summary: When the facial image is blurred, it has a great impact on high-level vision tasks such as face recognition.
This paper surveys and summarizes recently published methods for facial image deblurring, most of which are based on deep learning.
We show the performance of classical methods on datasets and metrics and give a brief discussion on the differences of model-based and learning-based methods.
- Score: 3.6775758132528877
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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