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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.

由于越来越多的计算方法和图像的各种含义的使用，形成具有聚焦的、空间分辨率的光的图像是一个快速创新的领域。除了像照片一样，通过光学手段获得的图像还可以显示距离、厚度、化学浓度、分子能量转移和许多其他性质，这取决于收集和分析的方法。在许多环境中，人们希望从很少的检测到的光形成图像；收集更多的光可能需要更多的能量、时间或空间，或者它可能会损坏正在研究的生物或分子样本。这个项目将开发新的方法，从很少的探测到的光形成计算图像。目标既有理论上的，也有实践上的：了解精确度和探测光量之间的基本权衡，并接近可能的最佳性能。该项目有可能改进远程遥感和各种形式的显微镜。光探测的半经典模型规定，光场强度转换为在单光子探测器输出观察到的泊松过程的速率。要充分利用这一流行模式，存在着巨大的智力挑战。虽然近年来在合并分段平滑(例如，小波域稀疏性)和自然信号中常见的其他结构方面取得了进展，但这一进展在很大程度上依赖于测量似然函数是高斯的。为泊松过程设计的信号处理方法并不常见，而且大多数方法是逐点应用的，而不是允许包含有用的信号先验。凸信号先验与泊松似然的天真组合可能导致难以处理的非凸优化。该项目将开发其他方法，特别是实时3D获取。