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International Journal of Computer Vision

Erschienen in:

25.06.2022

Deep Image Deblurring: A Survey

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

Erschienen in: International Journal of Computer Vision | Ausgabe 9/2022

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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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Titel: Deep Image Deblurring: A Survey
verfasst von: Kaihao Zhang
Wenqi Ren
Wenhan Luo
Wei-Sheng Lai
Björn Stenger
Ming-Hsuan Yang
Hongdong Li
Publikationsdatum: 25.06.2022
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 9/2022
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-022-01633-5

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