CASIA OpenIR  > 学术期刊  > Machine Intelligence Research
Rolling Shutter Camera: Modeling, Optimization and Learning
Bin Fan; Yuchao Dai; Mingyi He
发表期刊Machine Intelligence Research
ISSN2731-538X
2023
卷号20期号:6页码:783-798
摘要Most modern consumer-grade cameras are often equipped with a rolling shutter mechanism, which is becoming increasingly important in computer vision, robotics and autonomous driving applications. However, its temporal-dynamic imaging nature leads to the rolling shutter effect that manifests as geometric distortion. Over the years, researchers have made significant progress in developing tractable rolling shutter models, optimization methods, and learning approaches, aiming to remove geometry distortion and improve visual quality. In this survey, we review the recent advances in rolling shutter cameras from two aspects of motion modeling and deep learning. To the best of our knowledge, this is the first comprehensive survey of rolling shutter cameras. In the part of rolling shutter motion modeling and optimization, the principles of various rolling shutter motion models are elaborated and their typical applications are summarized. Then, the applications of deep learning in rolling shutter based image processing are presented. Finally, we conclude this survey with discussions on future research directions.
关键词Rolling shutter, motion modeling, image correction, temporal super-resolution, deep learning
DOI10.1007/s11633-022-1399-z
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被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/56010
专题学术期刊_Machine Intelligence Research
作者单位School of Electronics and Information, Northwestern Polytechnical University, Xi′an 710129, China
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Bin Fan,Yuchao Dai,Mingyi He. Rolling Shutter Camera: Modeling, Optimization and Learning[J]. Machine Intelligence Research,2023,20(6):783-798.
APA Bin Fan,Yuchao Dai,&Mingyi He.(2023).Rolling Shutter Camera: Modeling, Optimization and Learning.Machine Intelligence Research,20(6),783-798.
MLA Bin Fan,et al."Rolling Shutter Camera: Modeling, Optimization and Learning".Machine Intelligence Research 20.6(2023):783-798.
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