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

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，重新定义了相机，并超越了科学成像中的传统障碍。 成像已经改变了许多领域的科学和技术。 时间感知超快成像可以在未来几年带来进一步的革命性创新。 最近，将时间感知传感器转换成低成本消费者解决方案已经引起了巨大的商业兴趣。 展望未来，解决基于时间的正向和反向传输问题可以影响生物学、物理学、光学、计算机科学、工程学和数学等领域的新基础研究，并在健康、机器人、国防和移动性方面具有广泛的应用。他们有很大的潜力，刺激经济投资和创业使用现代成像解决方案。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诸如空间分辨率、波长和偏振的传感器参数。 然而，它们远低于光速，因此在模拟光传播的复杂动力学方面受到限制。 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）系统的激增，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尺度下每像素时间曲线的捕获和分析。这导致了对科学、工业和消费应用中的基本逆问题和解决方案的联合重新审视。具体来说，该项目构建了计算机视觉算法，用于查看视线之外、漫射层后面和混浊介质内部的物体。这在医学成像中提供了新的应用。随着理论基础和使能工具的发展，该项目加速了这一新领域的研究和商业化。

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.