Related papers: Deep Image Deblurring: A Survey

Deep Image Deblurring: A Survey

URL: http://arxiv.org/abs/2201.10700v1
Date: Wed, 26 Jan 2022 01:31:30 GMT
Title: Deep Image Deblurring: A Survey
Authors: Kaihao Zhang, Wenqi Ren, Wenhan Luo, Wei-Sheng Lai, Bjorn Stenger, Ming-Hsuan Yang, Hongdong Li
Abstract summary: Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image. Recent advances in deep learning have led to significant progress in solving this problem.
Score: 165.32391279761006
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Image deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image. Recent 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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