RI: Small: Lensless Cameras --- Enabling Novel Imaging Capabilities with Programmable Masks and Computational Imaging

RI：小型：无镜头相机 --- 通过可编程掩模和计算成像实现新颖的成像功能

基本信息

批准号：
1618823
负责人：
Aswin Sankaranarayanan
金额：
$ 35万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618823&HistoricalAwards=false
关键词：
RI Small Lensless Cameras Enabling

项目摘要

This project develops fundamentally new types of cameras that are extremely thin and flexible. Today, the primary technology for imaging and photography is by using a lens to focus light onto a planar sensor. However, this design requires a certain minimum distance - often, several millimeters - to focus the light from the scene to produce a high-resolution image. In addition, lenses are fundamentally incompatible for imaging on non-planar surfaces. By removing lenses from the imaging system, this project designs cameras that have thicknesses in the order of 100s of micrometers. This enables novel applications in mobile photography, endoscopy, microscopy, and the internet-of-things where there are stringent requirements on the thickness of the camera. Further, by exploiting advances in flexible electronics, the project is building novel camera designs for imaging on flexible and curved surfaces. The research in lens-less imaging is also incorporated into outreach activities in middle and high schools in the Greater Pittsburgh area, where workshops on camera building introduce students to basics concepts in optics and engineering while inspiring them to pursue a STEM career.This research focuses on designs for imaging at thin form factors and on curved and flexible surfaces. This is achieved by placing a carefully-designed mask in close proximity to the sensor. By analyzing the fundamental capabilities and limitations of mask-based imaging, highly optimized masks and associated reconstruction algorithms are designed to resolve scenes at high resolutions. Specifically, masks are optimized to deliver high light-throughput while providing invertible scene-to-sensor transfer functions. Programmable mask designs are also explored for enabling a richer space of transfer functions that are capable of acquiring not just images and videos, but also light fields. The research makes it possible to characterize the fundamental limits of thin form-factor imaging and the associated imaging architectures that achieve them.

该项目从根本上开发出非常薄且灵活的新型相机类型。如今，成像和摄影的主要技术是使用镜头将光线聚焦到平面传感器上。但是，这种设计需要一定的最小距离（通常是几毫米）才能将场景中的光聚焦以产生高分辨率图像。此外，镜片从根本上是在非平面表面上成像的不兼容。通过从成像系统中删除镜头，该项目设计的相机具有100毫米的厚度。这使得在移动摄影，内窥镜检查，显微镜和摄像机厚度有严格要求中的新颖应用程序中提供了新的应用。此外，通过利用柔性电子设备的进步，该项目正在构建新颖的相机设计，以在柔性和弯曲的表面上进行成像。在大匹兹堡地区的中学和高中，无镜头成像的研究还纳入了外展活动，在那里，相机建设的研讨会向学生介绍了光学和工程学领域的基础知识，同时激发了他们从事STEM职业的启发。这项研究重点是在薄型和曲线和柔性表面上进行成像的设计。这是通过将精心设计的面膜靠近传感器贴上来实现的。通过分析基于掩模的成像的基本功能和局限性，高度优化的面具和相关的重建算法旨在解决高分辨率的场景。具体而言，在提供可逆的场景到传感器传输功能的同时，对口罩进行了优化以提供高光通量。还探索了可编程的掩码设计，以实现更丰富的传输功能，这些功能不仅能够获取图像和视频，还可以获取光字段。这项研究使得可以表征薄形式因子成像的基本限制以及实现它们的相关成像体系结构。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation

DOI：
10.1109/iccphot.2019.8747330
发表时间：
2019-05
期刊：
2019 IEEE International Conference on Computational Photography (ICCP)
影响因子：
0
作者：
Yichen Wu;Vivek Boominathan;Huaijin Chen;Aswin C. Sankaranarayanan;A. Veeraraghavan
通讯作者：
Yichen Wu;Vivek Boominathan;Huaijin Chen;Aswin C. Sankaranarayanan;A. Veeraraghavan

SweepCam — Depth-Aware Lensless Imaging Using Programmable Masks