相关概念视频
您也可能阅读
相关文章
通过共同作者、期刊和引用图与本文相关的文章。
排序
Same author
Multi-Path Interference Challenges and Suggested Solution for Correlation-Assisted Direct Time-of-Flight.
Sensors (Basel, Switzerland)·2026
Same author
Defining the optimal imaging time point for fluorescence-lifetime-based tumor identification using the non-targeting near-infrared dye indocyanine green and post-processed high-dynamic-range images.
Biomedical optics express·2026
Same author
Reflect: reporting guidelines for preclinical, translational and clinical fluorescence molecular imaging studies.
Npj imaging·2025
Same author
Noise Analysis for Correlation-Assisted Direct Time-of-Flight.
Sensors (Basel, Switzerland)·2025
Same author
Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.
Sensors (Basel, Switzerland)·2025
Same author
Correlation-Assisted Pixel Array for Direct Time of Flight.
Sensors (Basel, Switzerland)·2024
Same journal
RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.
Sensors (Basel, Switzerland)·2026
Same journal
Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.
Sensors (Basel, Switzerland)·2026
Same journal
Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.
Sensors (Basel, Switzerland)·2026
Same journal
Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.
Sensors (Basel, Switzerland)·2026
Same journal
Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.
Sensors (Basel, Switzerland)·2026
Same journal
Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.
Sensors (Basel, Switzerland)·2026
相关实验视频
Updated: Jul 8, 2026

11:23
Lensless Fluorescent Microscopy on a Chip
Published on: August 17, 2011
18.1K
320 × 240 SPAD 直接飞行时间图像传感器和摄像机,基于像素内相关性和开关电容器平均值.
Maarten Kuijk1, Ayman Morsy1, Thomas Lapauw1
1ETRO.RDI, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
相关联辅助直飞时间 (CA-dToF) 成像现在可以在320x240 SPAD传感器上使用. 这种新的方法实现了高达6米的亚厘米精度深度传感,克服了以前的局限性.
科学领域:
- 光子学和传感技术的技术.
- 数字成像系统 数字成像系统
背景情况:
- 传统的飞行时间传感器在数据处理和通信方面遇到瓶.
- 像素内处理对于SPAD阵列的高效深度传感至关重要.
研究的目的:
- 在大格式的SPAD传感器上演示相关联辅助直飞时间 (CA-dToF) 成像.
- 为了实现高精度深度传感,降低数据处理开销.
主要方法:
- 使用320x240的SPAD阵列与集成的高速定时电路.
- 使用与光脉冲同步的直角三角波的像素内采样.
- 应用指数移动平均 (EMA) 到模拟电压用于飞行时间提取.
主要成果:
- 在6米的检测范围内,实现了亚厘米深度的精度.
- 证明有效的非相关的噪声源的平均值,只留下射击噪声.
- 在具有低光子检测效率 (PDE) 的QVGA传感器上成功实现了CA-dToF.
结论:
- CA-dToF是一种可行的技术,用于使用SPAD阵列进行高分辨率,远程深度传感.
- 像素内处理显著提高dToF系统的效率和可扩展性.
- 这一进步为更复杂的深度传感摄像头应用铺平了道路.

