课题基金 / 基金详情

CIF: Small: Sampling and Inference Methods for Spatiotemporal Single-Photon Imaging

CIF: Small: Sampling and Inference Methods for Spatiotemporal Single-Photon Imaging
CIF:小型:时空单光子成像的采样和推理方法
批准号:
1319140
负责人:
Yue Lu
金额:
$41.65万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-07-01 至 2017-12-31

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中文摘要
翻译
材料、器件和制造技术的最新进展导致了一类新兴的固态传感器,它们可以检测空间和时间中的单个光子。由于其单光子灵敏度,超快速度和快速增加的空间分辨率,这些新的单光子传感器(SPS)在广泛的应用中具有巨大的潜力,包括基于荧光的生物成像,飞行时间3D计算机视觉,高速摄像和天文学。该项目的目标是为基于新兴的SPS方案的光学成像建立严格的信号处理基础。类似于照相胶片中的卤化银颗粒,SPS的每个像素具有二进制响应,仅显示局部光强的一位和随机信息。利用像素阵列和高时间采样率,SPS生成大量时空比特,对原始视觉信息进行采样和编码。调查研究模型,理论和算法在信号采样和推理,以解决与SPS相关的挑战。本计画的具体目标包括:(1)建立特定时空频宽光强度场取样定理;(2)辨识效能界限,以及在成像效能与关键元件指标间的精确权衡;(3)设计自适应感测机制以改善成像效能;以及(4)开发能够有效地“解码”SPS生成的大量比特流的离线和在线图像形成算法。
英文摘要
Recent advances in materials, devices and fabrication technologies have led to an emerging class of solid-state sensors that can detect individual photons in space and time. Thanks to their single-photon sensitivity, ultra-fast speed, and rapidly increasing spatial resolutions, these new single photon sensors (SPS) have great potentials in a wide range of applications, including fluorescence-based bio-imaging, time-of-flight 3D computer vision, high-speed videography, and astronomy. The goal of this project is to build rigorous signal processing foundations for optical imaging based on the emerging SPS scheme.Analogous to silver-halide grains in photographic film, each pixel of the SPS has a binary response, revealing only one-bit and stochastic information of the local light intensity. With an array of pixels and high temporal sampling rates, the SPS generates a massive spatiotemporal volume of bits that sample and encode the original visual information. The investigator studies models, theory, and algorithms in signal sampling and inference to address the challenges associated with the SPS. Specific objectives of this project involve: (1) establishing sampling theorems for the SPS in acquiring light intensity fields of given spatiotemporal bandwidths; (2) identifying performance bounds and the precise tradeoffs between imaging performance and key device metrics; (3) designing adaptive sensing schemes to improve imaging performances; and (4) developing both offline and online image formation algorithms that can efficiently "decode" the massive bitstreams generated by the SPS.
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