Deep Image Deblurring: A Survey

• Published in: International journal of computer vision Vol. 130; no. 9; pp. 2103 - 2130
• Main Authors: Zhang, Kaihao, Ren, Wenqi, Luo, Wenhan, Lai, Wei-Sheng, Stenger, Björn, Yang, Ming-Hsuan, Li, Hongdong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022; Springer; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Blurring; Business performance management; Cameras; Computer Imaging; Computer Science; Computer vision; Datasets; Deep learning; Image Processing and Computer Vision; Literature reviews; Machine vision; Neural networks; Pattern Recognition; Pattern Recognition and Graphics; Performance measurement; Surveys; Taxonomy; Vision; Deep learning; Low-level vision; Image restoration; Image deblurring; Image enhancement
• ISSN: 0920-5691; 1573-1405
• DOI: 10.1007/s11263-022-01633-5

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.