Deep Image Deblurring: A Survey

Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei Sheng Lai, Björn Stenger, Ming Hsuan Yang, Hongdong Li

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2103-2130
Number of pages28
JournalInternational Journal of Computer Vision
Issue number9
Publication statusPublished - 2022 Sept

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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