A survey on facial image deblurring

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2023-11-30 DOI:10.1007/s41095-023-0336-6
Bingnan Wang, Fanjiang Xu, Quan Zheng
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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.

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人脸图像去模糊研究进展
当面部图像被模糊时,它会严重影响高级视觉任务,如面部识别。人脸图像去模糊的目的是从模糊的输入图像中恢复出清晰的图像,从而提高识别精度等。然而,一般的去模糊方法在面部图像上表现不佳。因此,人们提出了一些人脸去模糊方法,根据人脸图像的特点,通过添加语义或结构信息作为特定的先验来提高性能。在本文中,我们调查和总结了最近发表的面部图像去模糊的方法,其中大多数是基于深度学习的。首先,我们简要介绍了图像模糊的建模。接下来,我们将人脸去模糊方法归纳为两类:基于模型的方法和基于深度学习的方法。此外,我们总结了神经网络训练过程中常用的数据集、损失函数和性能评估指标。我们展示了经典方法在这些数据集和指标上的性能,并简要讨论了基于模型和基于学习的方法之间的差异。最后,讨论了当前面临的挑战和未来可能的研究方向。
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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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