CGV: Small: Collaborative Research: Diffractive masks and algorithms for light field capture

CGV：小型：协作研究：用于光场捕获的衍射掩模和算法

• 批准号: 1218409
• 负责人: Alyosha Molnar
• 金额: $ 25万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218409&HistoricalAwards=false
• 关键词: CGV; Small; Collaborative; Research; Diffractive

Diffractive masks and algorithms for light field capturePIs: Ramesh Raskar, MIT Media Lab Alyosha Molnar, Cornell ECEAdvanced imaging and display technology requires integrated, low cost systems able to efficiently capture and characterize light from 3-D scenes. In particular, a 3-D scene can be described by the collection of light rays it generates, called the light field. This research combines concepts from mask-based light-field imaging with angle sensitive pixels (ASPs). While mask-based light-field capture is much better understood mathematically, and masks are cheaper to manufacture and more easily modified on-the-fly, diffractive ASPs provide smaller, denser light field sensors, and provide naturally compressible outputs. This project combines the physics and signal processing of these approaches to enable optical imaging systems that capture more information than normal cameras while reducing the system's complexity. This work broadly impacts diverse applications spanning consumer imaging and displays, machine vision and automation, scientific/medical imaging and displays, robotic surgery, surveillance and remote sensing.3-D images and video can be captured by measuring the combined spatial and angular distribution of light (the light field). This research combines two techniques for light-field capture: mask-based light-field imaging and diffractive angle sensitive pixels (ASPs). A critical element of this work is the development of a mathematical framework that maps between conventional geometric light fields and the diffractive optics upon which ASPs rely. A second element is constructing hybrid systems based on this mathematics, leveraging diffractive effects in mask design, and combining masks with ASPs in single light-field cameras. This work also combines formalisms in existing light field methods with knowledge about real 3-D scene statistics to develop optimal (in the sense of usability and compressibility) basis sets for sampling and encoding the light-field. All of these aspects combine to reduce the size, cost and complexity of light field cameras, while simultaneously enhancing their capabilities.

光场捕获的衍射面具和算法：Ramesh Raskar，Mit Media Lab Alyosha Molnar，Cornell Eceadvanced Imaging和Display Technology需要集成的低成本系统，能够有效地捕获和表征来自3D场景的光。特别是，可以通过生成的光线收集（称为光场）来描述一个3-D场景。 这项研究结合了基于掩模的光场成像与角度敏感像素（ASP）的概念。 尽管基于掩模的光场捕获可以更好地理解数学上的理解，并且面罩的制造更便宜，并且更容易在直接进行的衍射ASP中更容易修改，可提供较小的较小的光场传感器，并提供自然可压缩的输出。 该项目结合了这些方法的物理和信号处理，以启用光学成像系统，这些光学成像系统比正常摄像机捕获更多的信息，同时降低系统的复杂性。 这项工作广泛地影响了消费者成像和显示，机器视觉和自动化，科学/医学成像和显示，机器人手术，监视和遥感的多样化应用。3-D图像和视频可以通过测量光场的空间和角度分布（光场）来捕获。 这项研究结合了两种用于光场捕获的技术：基于掩模的光场成像和衍射角敏感像素（ASP）。 这项工作的关键要素是开发数学框架，该框架映射了传统的几何光场和ASP依赖的衍射光学器件之间。 第二个要素是基于此数学，在掩盖设计中利用衍射效应，并将面膜与单一光场相机中的ASP结合起来，构建混合系统。 这项工作还将现有光场方法中的形式主义与有关真实3-D场景统计的知识结合在一起，以开发最佳（从可用性和可压缩性）基础集中进行采样和编码光场。所有这些方面结合在一起，以降低光场摄像机的尺寸，成本和复杂性，同时增强其功能。