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.

该项目开发了极其薄且灵活的新型相机。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。然而，这种设计需要一定的最小距离（通常为几毫米）来聚焦场景中的光线以产生高分辨率图像。此外，镜头根本不兼容在非平面表面上成像。通过从成像系统中移除镜头，该项目设计了厚度约为数百微米的相机。这使得移动摄影、内窥镜检查、显微镜检查和物联网等对相机厚度有严格要求的应用成为可能。此外，通过利用柔性电子技术的进步，该项目正在构建新颖的相机设计，用于在柔性和弯曲的表面上成像。无镜头成像的研究也被纳入大匹兹堡地区初中和高中的推广活动中，其中有关相机构建的研讨会向学生介绍光学和工程的基本概念，同时激励他们从事 STEM 职业。研究薄型尺寸以及弯曲和柔性表面上的成像设计。这是通过将精心设计的掩模放置在靠近传感器的位置来实现的。通过分析基于掩模的成像的基本功能和局限性，高度优化的掩模和相关的重建算法被设计用于以高分辨率解析场景。具体来说，掩模经过优化，可提供高光通量，同时提供可逆的场景到传感器传输功能。还探索了可编程掩模设计，以实现更丰富的传递函数空间，不仅能够获取图像和视频，还能够获取光场。该研究使得描述薄型成像的基本限制以及实现这些限制的相关成像架构成为可能。

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）

Compressive Video Sensing: Algorithms, architectures, and applications

压缩视频传感：算法、架构和应用

DOI：
10.1109/msp.2016.2602099
发表时间：
2024-09-13
期刊：
IEEE Signal Processing Magazine
影响因子：
14.9
作者：
Richard Baraniuk;T. Goldstein;Aswin C. Sankaranarayanan;Christoph Studer;A. Veeraraghavan;M. Wakin
通讯作者：
M. Wakin

PhaseCam3D — Learning Phase Masks for Passive Single View Depth Estimation

PhaseCam3D — 用于被动单视图深度估计的学习相位掩模

DOI：
10.1109/iccphot.2019.8747330
发表时间：
2019-05-01
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：
A. Veeraraghavan

SweepCam — Depth-Aware Lensless Imaging Using Programmable Masks

SweepCam — 使用可编程掩模的深度感知无透镜成像