CAREER: A Computational Hyperspectral Fluorescence Lifetime Camera

本计画发展一个计算影像系统，以选择性撷取萤光影像。许多化合物和材料都表现出荧光，并提供有关物体分子组成和微观结构的信息。虽然它通常不是眼睛直接可见的，但可以使用受控照明和滤光片使其可见。荧光对照明的快速响应只有特殊的超快相机才能看到。对这些信号的解释大大增强了传统成像能力，例如区分健康和癌组织，检测血液和其他体液，评估植物健康，将枪击残留物与枪支和火药制造商相匹配，识别绘画中使用的颜料，以及对矿物进行分类。然而，捕获所有不同的可能照明波长的荧光光谱沿着它们在图像中的每个像素的快速时间行为将导致涉及大量数据的高维数据集，因此是不切实际的。为了使荧光的使用，因此，该项目将设计一个灵活的计算成像系统，能够选择性地和自适应地捕获多维荧光信号的各个片段。智力优点在光学光场alongmultiple dimensions提供的高分辨率信息提出了一个根本性的挑战，所有的成像系统。可用于采样的数据量远远超过了即使是最先进的计算设备的收集、处理和存储能力。最终捕获它不仅受到技术能力的限制，而且还受到成像系统可用光子总数的限制。合理的是，这个问题的答案是设计成像系统，分析特定于成像任务的光。成像系统被设计为在计算特征空间和可用硬件组件上执行联合优化以执行选择性感测。更广泛的影响这个项目将教育外行和学生关于荧光的流行，荧光寿命和其他"隐藏"的光谱现象在日常场景中。我们也将提供一个重要的一步，在特定的应用方向，高维选择性成像系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project develops a computational imaging system for the selective capture of fluorescence images. Fluorescence is exhibited by many compounds and materials and provides information about the objects' molecular makeup and microstructure. While not usually directly visible to the eye, it can be made visible using controlled illumination and optical filters. The fast response of fluorescence to illumination is visible only to special ultrafast cameras. Interpretation of these signals significantly enhances conventional imaging capabilities, for example to distinguish between healthy and cancerous tissue, detect blood and other bodily fluids, assess plant health, match gunshot residue to a gun and gunpowder manufacturer, identify the paints used in paintings, and classify minerals. However, capturing the fluorescence spectra for all different possible illumination wavelengths along with their fast temporal behavior for each pixel in an image would lead to a high dimensional dataset involving immense amounts of data and is therefore impractical. To enable the use of fluorescence, this project therefore will design a flexible computational imaging system that is able to selectively and adaptively capture individual pieces of the multidimensional fluorescence signal.Intellectual Merit The high resolution information available in the optical light field alongmultiple dimensions presents a fundamental challenge to all imaging systems. The amount ofdata available for sampling far exceeds the collection, processing and storage capacities of eventhe most advanced computing equipment. Capturing it ultimately is limited not only by technical capabilities, but also by the total number of photons available to the imaging system. It is reasonable that the answer to this problem is to design imaging systems that analyze light specific to an imaging task. Imaging systems are designed where a joint optimization is performed over computational feature space and available hardware components to perform selective sensing.Broader Impacts This project will educate laymen and students about the prevalence of fluorescence, fluorescence lifetime and other "hidden" spectral phenomena in everyday scenes. We will also provide an important step in the direction of application specific, high dimensional selective imaging systems. This will result in smaller and more practical computational imaging and compressive sensing implementations.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.