Rolling Shutter Camera:Modeling,Optimization and Learning  

作　　者：Bin Fan Yuchao Dai Mingyi He 

机构地区：[1]School of Electronics and Information,Northwestern Polytechnical University,Xi’an,710129,China

出　　处：《Machine Intelligence Research》2023年第6期783-798,共16页机器智能研究（英文版）

基　　金：This work was supported in part by National Natural Science Foundation of China(Nos.62271410,61901387 and 62001394);the Fundamental Research Funds for the Central Universities,China,and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China(No.CX2022046).

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]