Details

Autor(en) / Beteiligte

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

Titel

Deep Image Deblurring: A Survey

Ist Teil von

• International journal of computer vision, 2022-09, Vol.130 (9), p.2103-2130

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2022

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• 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.