A survey on facial image deblurring

TL;DR

This survey reviews recent deep learning-based facial image deblurring methods, discussing datasets, loss functions, and evaluation metrics, highlighting their performance and future challenges in improving face recognition accuracy.

Contribution

It provides a comprehensive overview of face deblurring techniques, categorizing methods, and analyzing their performance, which aids future research in this area.

Findings

Deep learning methods outperform classical techniques on standard datasets.

Semantic priors improve face deblurring accuracy.

Current challenges include dataset diversity and real-world applicability.

Abstract

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.