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

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先进的成像和显示技术需要能够有效捕获和表征来自3-D场景的光的集成、低成本系统。具体地说，3-D场景可以通过它产生的光线集合来描述，称为光场。这项研究结合了基于掩模的光场成像和角度敏感像素(ASP)的概念。虽然基于掩模的光场捕获在数学上更容易理解，而且掩模的制造成本更低，更容易动态修改，但衍射式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。这项工作广泛影响各种应用，包括消费者成像和显示、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。可以通过测量光的组合空间和角度分布(光场)来捕获3D图像和视频。这项研究结合了两种光场捕获技术：基于掩模的光场成像和衍射角敏感像素(ASP)。这项工作的一个关键要素是建立一个数学框架，在传统的几何光场和ASP所依赖的衍射光学之间进行映射。第二个要素是在此数学基础上构建混合系统，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。这项工作还将现有光场方法中的形式化与真实3D场景统计的知识相结合，以开发用于采样和编码光场的最优(在可用性和可压缩性意义上)基集。所有这些方面结合在一起，降低了光场相机的尺寸、成本和复杂性，同时提高了它们的能力。

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.