A survey on facial image deblurring  

作　　者：Bingnan Wang Fanjiang Xu Quan Zheng 

机构地区：[1]Institute of Software,Chinese Academy of Sciences,Beijing,100190,China [2]University of Chinese Academy of Sciences,Beijing,100049,China

出　　处：《Computational Visual Media》2024年第1期3-25,共23页计算可视媒体（英文版）

基　　金：We acknowledge the support from the research grants No.E2RC5901 and No.E3KW5902.

摘　　要：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.

关 键 词：facial image deblurring MODEL-BASED deep learning-based semantic or structural prior 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]