CIF: Small: Low-Light 3D Imaging: From Fundamental Limits to Practical Systems

CIF：小型：低光 3D 成像：从基本限制到实用系统

基本信息

批准号：
1422034
负责人：
Vivek Goyal
金额：
$ 46.52万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422034&HistoricalAwards=false
关键词：
CIF Small Low Light 3D

项目摘要

Forming images with focused, spatially resolved light is an area of rapid innovation due to the growing use of computational methods and the variety of meanings of ?image.? Aside from being photograph-like, an image acquired through optical means could show distances, thicknesses, chemical concentrations, molecular energy transfers and many other properties, depending on the methods of collection and analysis. In many settings, it is desirable to form images from very little detected light; collecting more light could take more energy, time, or space, or it could damage a biological or molecular sample under investigation. This project will develop new methods for computational image formation from very little detected light. The goals are both theoretical and practical: to understand the fundamental trade-offs between accuracy and amount of detected light, and to approach the best possible performance. The project has the potential to improve long-range remote sensing as well as various forms of microscopy.The semiclassical model of light detection dictates that light field intensity translates to the rate of a Poisson process observed at a single-photon detector output. There are significant intellectual challenges in fully exploiting this prevailing model. While progress has been made in recent years to incorporate piecewise smoothness (e.g., wavelet-domain sparsity) and other structure that is commonly found in natural signals, this progress has largely relied on measurement likelihood functions being Gaussian. Signal processing methods designed for Poisson processes are uncommon, and most of them apply pointwise rather than to allow the inclusion of useful signal priors. Naive combinations of convex signal priors with Poisson likelihoods can lead to intractable nonconvex optimizations. This project will develop alternative approaches, especially toward real-time 3D acquisition.

由于计算方法的使用越来越多以及图像的多种含义。除了拍照外，通过光学手段获得的图像还可以显示距离，厚度，化学浓度，分子能量转移以及许多其他特性，具体取决于收集和分析的方法。在许多设置中，希望从很少检测到的光中形成图像。收集更多的光可能需要更多的能量，时间或空间，或者可能损害正在研究的生物学或分子样本。该项目将从很少检测到的光中开发出用于计算图像形成的新方法。目标既是理论上的又是实用的：要了解准确性和检测到的光量之间的基本权衡，并达到最佳性能。该项目有可能改善远程遥感以及各种形式的显微镜。光场检测的半经典模型决定，光场强度转化为在单光子检测器输出下观察到的泊松过程的速率。充分利用这一现行模型存在重大的智力挑战。尽管近年来已经取得了进展，以结合分段平滑度（例如小波域的稀疏性）和其他自然信号中常见的结构，但这种进步在很大程度上取决于测量的可能性功能是高斯。专为泊松过程设计的信号处理方法并不常见，其中大多数是偶然应用的，而不是允许包含有用的信号先验。凸信号先验与泊松可能性的幼稚组合会导致棘手的非凸优化。该项目将开发替代方法，尤其是针对实时3D获取的方法。