1. Deep Image Deblurring: A Survey.
- Author
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Zhang, Kaihao, Ren, Wenqi, Luo, Wenhan, Lai, Wei-Sheng, Stenger, Björn, Yang, Ming-Hsuan, and Li, Hongdong
- Subjects
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COMPUTER vision , *CONVOLUTIONAL neural networks , *STEREO image , *IMAGE reconstruction , *DEEP learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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