Application of Deep Learning in Blind Motion Deblurring: Current Status
and Future Prospects
- URL: http://arxiv.org/abs/2401.05055v1
- Date: Wed, 10 Jan 2024 10:30:18 GMT
- Title: Application of Deep Learning in Blind Motion Deblurring: Current Status
and Future Prospects
- Authors: Yawen Xiang, Heng Zhou, Chengyang Li, Fangwei Sun, Zhongbo Li and
Yongqiang Xie
- Abstract summary: Blind motion deblurring aims to restore clear and detailed images without prior knowledge of the blur type.
This paper provides an exhaustive overview of the role of deep learning in blind motion deblurring, encompassing datasets, evaluation metrics, and methods developed over the last six years.
- Score: 7.198959621445282
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Motion deblurring is one of the fundamental problems of computer vision and
has received continuous attention. The variability in blur, both within and
across images, imposes limitations on non-blind deblurring techniques that rely
on estimating the blur kernel. As a response, blind motion deblurring has
emerged, aiming to restore clear and detailed images without prior knowledge of
the blur type, fueled by the advancements in deep learning methodologies.
Despite strides in this field, a comprehensive synthesis of recent progress in
deep learning-based blind motion deblurring is notably absent. This paper fills
that gap by providing an exhaustive overview of the role of deep learning in
blind motion deblurring, encompassing datasets, evaluation metrics, and methods
developed over the last six years. Specifically, we first introduce the types
of motion blur and the fundamental principles of deblurring. Next, we outline
the shortcomings of traditional non-blind deblurring algorithms, emphasizing
the advantages of employing deep learning techniques for deblurring tasks.
Following this, we categorize and summarize existing blind motion deblurring
methods based on different backbone networks, including convolutional neural
networks, generative adversarial networks, recurrent neural networks, and
Transformer networks. Subsequently, we elaborate not only on the fundamental
principles of these different categories but also provide a comprehensive
summary and comparison of their advantages and limitations. Qualitative and
quantitative experimental results conducted on four widely used datasets
further compare the performance of SOTA methods. Finally, an analysis of
present challenges and future pathways. All collected models, benchmark
datasets, source code links, and codes for evaluation have been made publicly
available at https://github.com/VisionVerse/Blind-Motion-Deblurring-Survey
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