实时非视线计算成像使用频谱过和运动补偿
Jun-Tian Ye1,2,3, Yi Sun1,2,3, Wenwen Li1,2,3
1Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences, University of Science and Technology of China, Hefei, China.
Nature computational science
|November 6, 2024
概括
这项研究引入了非视线 (NLOS) 成像的新框架,使复杂场景中隐藏物体的实时视频捕捉成为可能. 该方法使用频谱过和运动补偿来获得高质量,动态的NLOS视频.
科学领域:
- 光学和光子学 在光学和光子学.
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 非视线 (NLOS) 成像可以恢复隐藏的物体细节.
- 动态场景的实时视频是具有挑战性的,因为弱的多重散射光.
研究的目的:
- 为在房间大小的环境中开发高质量,实时NLOS视频的框架.
- 为了克服NLOS成像中弱信号和动态场景复杂性的局限性.
主要方法:
- 一个新的框架,结合了频谱过和运动补偿.
- 频谱过使用基于波的模型来消除频域的模糊和模糊.
- 带有交叉扫描的运动补偿可以从低质量的序列中实现实时视频.
主要成果:
- 证明了高质量的NLOS视频在每秒4 (fps).
- 成功捕捉到各种各样的动态现实生活场景.
- 通过最小的采样点实现了计算图像重建.
结论:
- 该框架代表了实时,大规模NLOS成像的重大进步.
- 能够在低功耗的NLOS成像和传感中实现实际应用.
- 为未来隐藏对象恢复领域的发展铺平了道路.
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