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

这个项目开发了一种全新的相机，非常薄，非常灵活。今天，成像和摄影的主要技术是使用透镜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——通常是几毫米——来聚焦来自场景的光线，从而产生高分辨率的图像。此外，透镜在非平面表面成像是根本不兼容的。通过从成像系统中移除镜头，该项目设计出了厚度为100微米的相机。这使得在移动摄影、内窥镜、显微镜和物联网等对相机厚度有严格要求的领域有了新的应用。此外，通过利用柔性电子技术的进步，该项目正在构建用于在柔性曲面上成像的新型相机设计。无镜头成像的研究也被纳入了大匹兹堡地区的初中和高中的推广活动，在那里，相机制造研讨会向学生介绍光学和工程的基本概念，同时激励他们追求STEM职业。本研究的重点是在薄形状因素和弯曲和柔性表面上的成像设计。这是通过在传感器附近放置一个精心设计的掩模来实现的。通过分析基于掩模成像的基本能力和局限性，设计了高度优化的掩模和相关重建算法，以高分辨率解决场景。具体来说，掩模经过优化，可以提供高光吞吐量，同时提供可逆的场景到传感器传递函数。还探索了可编程掩模设计，以实现更丰富的传递函数空间，不仅可以获取图像和视频，还可以获取光场。这项研究使得描述薄尺寸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。

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.