RI: Small: Time Resolved Imaging: New Methods for Capture, Analysis and Applications

RI：小型：时间分辨成像：捕获、分析和应用的新方法

• 批准号: 1527181
• 负责人: Ramesh Raskar
• 金额: $ 46万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527181&HistoricalAwards=false
• 关键词: RI; Small; Time; Resolved; Imaging

This project fundamentally combines the emerging time of flight imaging techniques with computational methods to redefine a camera and also go beyond the conventional barriers in scientific imaging. Imaging has transformed science and technology in many fields. Time-aware ultrafast imaging can bring further radical new innovations in coming years. Recently, there has been a significant commercial interest in converting time-aware sensors into low cost consumer solutions. Going forward, solving time-based forward and inverse transport problems can impact new fundamental research in biology, physics, optics, computer science, engineering, and mathematics, with broad applications in health, robotics, defense, and mobility. They have high potential to stimulate economic investment and entrepreneurship using modern imaging solutions. Emerging image sensors with picosecond (ps) time resolution provide new ways to capture and understand the world. For scene analysis, typical computational imaging techniques exploit sensor parameters such as spatial resolution, wavelength, and polarization. However, they are far slower than light speed and are consequently limited in their ability to model the complex dynamics of light propagation. Time-resolved (or transient) sensors overcome this limitation, but their integration with computational methods has not been realized yet. Therefore, with the recent spurt in commercial time-of-flight (ToF) systems, new research in transient computational imaging is well-timed. Beyond ToF depth information, this research explores the capture and analysis of per-pixel time profiles at ps scales. This leads to joint re-examination of fundamental inverse problems and solutions in scientific, industrial and consumer applications. Specifically the project builds computer vision algorithms for seeing objects beyond the line of sight, behind diffusive layers and inside turbid media. This provides novel applications in medical imaging. With the development of the theoretical foundation and enabling tools, the project accelerates research and commercialization of this new field.

该项目从根本上将新兴的飞行时间成像技术与计算方法相结合，重新定义相机，并超越科学成像的传统障碍。 成像技术改变了许多领域的科学技术。 时间感知超快成像可以在未来几年带来进一步彻底的创新。 最近，人们对将时间感知传感器转变为低成本消费解决方案产生了巨大的商业兴趣。 展望未来，解决基于时间的正向和逆向传输问题可能会影响生物学、物理学、光学、计算机科学、工程和数学领域的新基础研究，并在健康、机器人、国防和移动领域具有广泛的应用。它们具有利用现代成像解决方案刺激经济投资和创业的巨大潜力。具有皮秒 (ps) 时间分辨率的新兴图像传感器提供了捕捉和理解世界的新方法。对于场景分析，典型的计算成像技术利用空间分辨率、波长和偏振等传感器参数。 然而，它们的速度远慢于光速，因此模拟光传播的复杂动力学的能力受到限制。 时间分辨（或瞬态）传感器克服了这一限制，但它们与计算方法的集成尚未实现。 因此，随着最近商业飞行时间（ToF）系统的蓬勃发展，瞬态计算成像的新研究恰逢其时。除了 ToF 深度信息之外，本研究还探索了 ps 尺度上每像素时间剖面的捕获和分析。这导致科学、工业和消费应用中的基本逆问题和解决方案的联合重新检查。具体来说，该项目构建了计算机视觉算法，用于观察视线之外、扩散层后面和浑浊介质内部的物体。这为医学成像提供了新颖的应用。随着理论基础和支持工具的发展，该项目加速了这一新领域的研究和商业化。