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Deep Image Deblurring: A Survey

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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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Acknowledgements

This research was funded in part by the NSF CAREER Grant #1149783, ARC-Discovery grant projects (DP 190102 261 and DP220100800), and a Ford Alliance URP grant.

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Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Kaihao Zhang & Hongdong Li

  2. Sun Yat-sen University, Guangzhou, 510275, China

    Wenqi Ren & Wenhan Luo

  3. Rakuten Institute of Technology, Rakuten Group Inc., Tokyo, Japan

    Björn Stenger

  4. School of Engineering, University of California at Merced, Merced, CA, USA

    Wei-Sheng Lai & Ming-Hsuan Yang

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  1. Kaihao Zhang
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  2. Wenqi Ren
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  3. Wenhan Luo
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  4. Wei-Sheng Lai
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  6. Ming-Hsuan Yang
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  7. Hongdong Li
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Correspondence to Ming-Hsuan Yang.

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Communicated by Jiaya Jia.

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Zhang, K., Ren, W., Luo, W. et al. Deep Image Deblurring: A Survey. Int J Comput Vis 130, 2103–2130 (2022). https://doi.org/10.1007/s11263-022-01633-5

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