All-in-Focus Imaging from Event Focal Stack

Hanyue Lou, Minggui Teng, Yixin Yang, Boxin Shi
{"title":"All-in-Focus Imaging from Event Focal Stack","authors":"Hanyue Lou, Minggui Teng, Yixin Yang, Boxin Shi","doi":"10.1109/CVPR52729.2023.01666","DOIUrl":null,"url":null,"abstract":"Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.","PeriodicalId":376416,"journal":{"name":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR52729.2023.01666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
来自事件焦点堆栈的全焦成像
传统的焦点叠加方法需要多次拍摄同一场景中不同距离聚焦的图像,不能很好地应用于动态场景。由于单张图像的散焦和去模糊问题的高度病态性质,从一张照片中生成高质量的全焦图像是具有挑战性的。在本文中,为了恢复全焦图像,我们提出了事件焦点堆栈,它被定义为在连续焦点扫描期间捕获的事件流。给定任意距离聚焦的RGB图像,我们探索事件流的高时间分辨率,从中自动选择重新聚焦的时间戳,并将相应的重新聚焦的图像与事件重建以形成焦点堆栈。在所选时间戳周围相邻事件的引导下,我们可以将焦点堆栈与适当的权重合并,并恢复清晰的全焦图像。在合成数据集和真实数据集上的实验结果都显示出优于最先进方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
L-CoIns: Language-based Colorization With Instance Awareness Neural Texture Synthesis with Guided Correspondence LOGO: A Long-Form Video Dataset for Group Action Quality Assessment ERM-KTP: Knowledge-Level Machine Unlearning via Knowledge Transfer Target-referenced Reactive Grasping for Dynamic Objects
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1