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NSF-BSF: HCC: Small: Computational Imaging with Speckle Correlations for Material Analysis

NSF-BSF：HCC：小型：用于材料分析的散斑相关性计算成像

基本信息

批准号：
2008123
负责人：
Ioannis Gkioulekas
金额：
$ 49.99万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008123&HistoricalAwards=false
关键词：
NSF BSF HCC Small Computational

项目摘要

Light scattering materials are ubiquitous: from biological tissues and minerals to the atmosphere and clouds, cosmetics, and many industrial chemicals. When viewed under coherent imaging conditions such as laser illumination, these materials create "noisy" images containing small spurious spots of color known as speckle, which severely degrade image quality. Despite their seemingly random nature, speckle images have strong statistical properties that are highly informative of the material producing them, and can be used to enable remarkable imaging capabilities (for example, being able to see through or focus inside a material). However, realizing these capabilities in practical application settings where scattering is important (tissue imaging, fluorescence microscopy, remote sensing, material fabrication) remains a challenge due to our incomplete understanding of speckle statistical properties. This project will use techniques from computer vision and computer graphics to greatly enhance our understanding of these properties, and significantly expand the scope of possible applications, thereby having transformative impact in areas such as medicine and material science. The project will additionally create a new point of convergence between vision, graphics, optics, and imaging, establishing new research directions and methodological approaches within and across these areas.Achieving the project's goals will require developing a better understanding of the limitations inherent in speckle statistics, and using this knowledge to invent better imaging algorithms. To date, efforts towards this direction have been hindered by difficulties in simulating speckle effects stemming from the complexity of the wave-optics and multiple-scattering phenomena underlying them. This project will change this state of affairs through three tightly coupled research thrusts: 1) Simulation The project will first develop Monte Carlo rendering algorithms that efficiently simulate physically accurate speckle images and different types of statistics (spatial, temporal, spectral). These tools will be used to perform a thorough qualitative and quantitative investigation of the statistical properties of speckle images, with emphasis on application-relevant settings. Phenomenological discoveries will be backed by theoretical analysis of the underlying physics. 2) The project will develop new algorithms for speckle-based imaging through scattering, using the insights from the exploration of speckle statistics. These algorithms will be designed to specifically exploit additional statistical structure in speckle images that currently remains untapped. The improved performance of the developed algorithms will be demonstrated through experiments on tissue phantoms and under conditions emphasizing fluorescence imaging applications. 3)The project will develop computational imaging systems that use the rich information available in speckle images to characterize the optical scattering properties of material samples (for instance, for material quality control). The use of speckle measurements will endow these systems with a combination of accuracy, efficiency, and generality that is not available in existing scattering acquisition technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

光散射材料无处不在：从生物组织和矿物到大气和云，化妆品和许多工业化学品。当在相干成像条件下观察时，如激光照明，这些材料会产生“噪声”图像，其中包含称为散斑的小伪彩色斑点，严重降低图像质量。尽管散斑图像看似随机，但它们具有很强的统计特性，这些特性可以提供产生它们的材料的高度信息，并且可以用于实现卓越的成像能力（例如，能够透视或聚焦材料内部）。然而，在散射很重要的实际应用环境（组织成像，荧光显微镜，遥感，材料制造）中实现这些功能仍然是一个挑战，因为我们对散斑统计特性的理解不完全。该项目将使用计算机视觉和计算机图形学技术，大大提高我们对这些特性的理解，并显着扩大可能的应用范围，从而在医学和材料科学等领域产生变革性影响。该项目还将在视觉、图形、光学和成像之间建立一个新的交汇点，在这些领域内和跨这些领域建立新的研究方向和方法论途径。实现该项目的目标将需要更好地理解散斑统计固有的局限性，并利用这些知识发明更好的成像算法。迄今为止，朝着这个方向的努力一直受到阻碍的困难，在模拟散斑效应源于复杂的波动光学和多重散射现象的基础上。该项目将通过三个紧密耦合的研究重点来改变这种状况：1）模拟该项目将首先开发蒙特卡罗渲染算法，有效地模拟物理上精确的斑点图像和不同类型的统计数据（空间，时间，光谱）。这些工具将用于对散斑图像的统计特性进行全面的定性和定量研究，重点是与应用相关的设置。现象学的发现将得到基础物理学的理论分析的支持。2)该项目将开发新的算法，通过散射斑点成像，利用斑点统计的探索的见解。这些算法将被设计为专门利用目前尚未开发的散斑图像中的额外统计结构。所开发的算法的改进性能将通过组织幻影和强调荧光成像应用的条件下的实验来证明。3)该项目将开发计算成像系统，利用散斑图像中的丰富信息来表征材料样品的光学散射特性（例如，用于材料质量控制）。散斑测量的使用将赋予这些系统以现有散射采集技术所不具备的准确性、效率和通用性的组合。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。