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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| Published in | International journal of computer vision Vol. 130; no. 9; pp. 2103 - 2130 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2022
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0920-5691 1573-1405 |
| DOI | 10.1007/s11263-022-01633-5 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Audience | Academic |
| Author | Lai, Wei-Sheng Ren, Wenqi Zhang, Kaihao Luo, Wenhan Yang, Ming-Hsuan Li, Hongdong Stenger, Björn |
| Author_xml | – sequence: 1 givenname: Kaihao surname: Zhang fullname: Zhang, Kaihao organization: Australian National University – sequence: 2 givenname: Wenqi surname: Ren fullname: Ren, Wenqi organization: Sun Yat-sen University – sequence: 3 givenname: Wenhan surname: Luo fullname: Luo, Wenhan organization: Sun Yat-sen University – sequence: 4 givenname: Wei-Sheng surname: Lai fullname: Lai, Wei-Sheng organization: School of Engineering, University of California at Merced – sequence: 5 givenname: Björn surname: Stenger fullname: Stenger, Björn organization: Rakuten Institute of Technology, Rakuten Group Inc – sequence: 6 givenname: Ming-Hsuan orcidid: 0000-0003-4848-2304 surname: Yang fullname: Yang, Ming-Hsuan email: mhyang@ucmerced.edu organization: School of Engineering, University of California at Merced – sequence: 7 givenname: Hongdong surname: Li fullname: Li, Hongdong organization: Australian National University |
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| Keywords | Deep learning Low-level vision Image restoration Image deblurring Image enhancement |
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| Title | Deep Image Deblurring: A Survey |
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