Deep Image Deblurring: A Survey

Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a...

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Bibliographic Details
Published in:International journal of computer vision 2022-09, Vol.130 (9), p.2103-2130
Main Authors: Zhang, Kaihao, Ren, Wenqi, Luo, Wenhan, Lai, Wei-Sheng, Stenger, Björn, Yang, Ming-Hsuan, Li, Hongdong
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Blurring
Business performance management
Cameras
Computer Imaging
Computer Science
Computer vision
Datasets
Deep learning
Image Processing and Computer Vision
Literature reviews
Machine vision
Neural networks
Pattern Recognition
Pattern Recognition and Graphics
Performance measurement
Surveys
Taxonomy
Vision
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Summary:Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions.
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-022-01633-5