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.

用于光场捕获的衍射掩模和算法PI：Ramesh Raskar，麻省理工学院媒体实验室Alyosha Molnar，康奈尔大学ECE高级成像和显示技术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。特别地，3D场景可以通过它生成的光线的集合来描述，称为光场。 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。 虽然基于掩模的光场捕获在数学上得到了更好的理解，并且掩模制造成本更低，更容易在运行中修改，但衍射ASP提供了更小，更密集的光场传感器，并提供了自然可压缩的输出。 该项目结合了这些方法的物理和信号处理，使光学成像系统能够捕获比普通相机更多的信息，同时降低系统的复杂性。 这项工作广泛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，监控和远程传感等各种应用。3-D图像和视频可以通过测量光的组合空间和角分布（光场）来捕获。 本研究结合两种光场撷取技术：光罩式光场成像与绕射角度敏感像素（ASP）。 这项工作的一个关键因素是一个数学框架的发展，传统的几何光场和衍射光学依赖ASP之间的映射。 第二个要素是基于这种数学方法构建混合系统，在掩模设计中利用衍射效应，并在单光场相机中将掩模与ASP相结合。 这项工作还结合了现有的光场方法的形式主义与知识的真实的3-D场景统计开发最佳的（在这个意义上的可用性和可压缩性）的基础集的采样和编码的光场。所有这些方面的联合收割机结合在一起，减少了光场相机的尺寸、成本和复杂性，同时增强了它们的能力。