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arXiv · 2201.10700

Deep Image Deblurring: A Survey

Kaihao Zhang·Wenqi Ren·Wenhan Luo·Wei-Sheng Lai·Bjorn Stenger·Ming-Hsuan Yang·Hongdong Li
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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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Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, Bjorn Stenger, Ming-Hsuan Yang, Hongdong Li. 2022-01-26. Deep Image Deblurring: A Survey. https://arxiv.org/abs/2201.10700

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