CAREER: A Signal Processing Framework for Computational Imaging: From Theory to Applications

职业：计算成像信号处理框架：从理论到应用

• 批准号: 1652633
• 负责人: Ashok Veeraraghavan
• 金额: $ 54.9万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2023-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652633&HistoricalAwards=false
• 关键词: CAREER; Signal; Processing; Framework; Computational

Computational Imaging (CI) has emerged as a powerful tool for the design of imaging systems over a wide variety of applications, including consumer imaging, machine vision, surveillance, remote sensing, and microscopy. The key idea behind CI is that joint design and optimization of optics, sensors and algorithms rapidly enlarges the design space for imaging enabling performance characteristics that were hitherto unachievable. This project develops novel imaging systems that can break through the barriers of size, spatial resolution, temporal resolution, field of view, weight and cost by leveraging on the co-design of optics and signal processing. Imaging as such, has a very broad range of applications spanning consumer/medical/scientific imaging, machine vision and remote sensing. In conjunction with advancing the scientific goals of this project, the project integrates computational imaging and display concepts into several courses offered at Rice University and is developing a summer workshop on imaging to economically disadvantaged high school students and their teachers, thereby increasing K-12 STEM engagement. This research develops a signal processing framework for Computational Imaging (co-design of optics, sensors and algorithms), that is based on Gaussian Mixture Models. The model along with associated algorithms, will, for the first time, enable (a) fundamental performance bounds for existing CI systems, (b) make the design process systematic and develop novel CI systems, and (c) characterize and understand the regimes of operation for CI. The research team demonstrates the importance and utility of the framework, by creating order of magnitude performance improvements in three different application areas: (1) lens-free cameras: design, develop and demonstrate thin, lens-free cameras that consist of a series of masks laid on top of a bare conventional sensor with associated algorithms for demultiplexing; (2) light-in-flight imaging: design, develop and demonstrate ultra-fast imaging using conventional time-of-flight sensors and appropriate illumination/sensor modulation and reconstruction techniques; and (3) high-res, long-range imaging: design, develop and demonstrate sub-diffraction imaging resolution at long ranges, using a camera array and phase retrieval based algorithms.

计算成像(CI)已成为设计成像系统的强大工具，应用范围广泛，包括消费者成像、机器视觉、监视、遥感和显微镜。CI背后的关键思想是，光学、传感器和算法的联合设计和优化迅速扩大了成像使能性能特性的设计空间，这是迄今为止无法实现的。该项目开发了新型成像系统，通过利用光学和信号处理的共同设计，可以突破尺寸、空间分辨率、时间分辨率、视场、重量和成本的障碍。因此，成像具有非常广泛的应用范围，涵盖消费/医疗/科学成像、机器视觉和遥感。在推进该项目的科学目标的同时，该项目将计算成像和显示概念整合到莱斯大学提供的几门课程中，并正在为经济困难的高中生及其教师开发一个关于成像的暑期讲习班，从而增加K-12 STEM的参与度。这项研究为计算成像(光学、传感器和算法的联合设计)开发了一个基于高斯混合模型的信号处理框架。该模型和相关算法将首次实现(A)现有CI系统的基本性能界限，(B)使设计过程系统化并开发新的CI系统，以及(C)表征和理解CI的运行机制。研究小组通过在三个不同的应用领域创造数量级的性能改进，证明了该框架的重要性和实用性：(1)无镜头相机：设计、开发和演示由放置在裸露的传统传感器上的一系列面具以及相关的多路分解算法组成的薄型无镜头相机；(2)飞行中光成像：使用传统飞行时间传感器和适当的照明/传感器调制和重建技术设计、开发和演示超快成像；以及(3)高分辨率、远距离成像：使用相机阵列和基于相位恢复的算法，设计、开发和演示远距离亚衍射成像分辨率。

项目成果

Systematic analysis of video-based pulse measurement from compressed videos

压缩视频中基于视频的脉冲测量的系统分析

• DOI: 10.1364/boe.408471
• 发表时间: 2020
• 期刊: Biomedical Optics Express
• 影响因子: 3.4
• 作者: Nowara, Ewa M.;McDuff, Daniel;Veeraraghavan, Ashok
• 通讯作者: Veeraraghavan, Ashok

Deep-3D microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning

• DOI: 10.1364/boe.444488
• 发表时间: 2022-01-01
• 期刊: BIOMEDICAL OPTICS EXPRESS
• 影响因子: 3.4
• 作者: Li, Bowen;Tan, Shiyu;Veeraraghavan, Ashok
• 通讯作者: Veeraraghavan, Ashok

Face Detection and Verification Using Lensless Cameras

• DOI: 10.1109/tci.2018.2889933
• 发表时间: 2019-06
• 期刊: IEEE Transactions on Computational Imaging
• 影响因子: 5.4
• 作者: Jasper Tan;Li Niu;Jesse K. Adams;Vivek Boominathan;Jacob T. Robinson;Richard Baraniuk;A. Veeraraghavan

FlatNet3D: intensity and absolute depth from single-shot lensless capture

• DOI: 10.1364/josaa.466286
• 发表时间: 2022-10-01
• 期刊: JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
• 影响因子: 1.9
• 作者: Bagadthey, Dhruvjyoti;Prabhu, Sanjana;Mitra, Kaushik
• 通讯作者: Mitra, Kaushik

WISHED: Wavefront imaging sensor with high resolution and depth ranging

• DOI: 10.1109/iccp48838.2020.9105280
• 发表时间: 2020-04
• 期刊: 2020 IEEE International Conference on Computational Photography (ICCP)
• 作者: Yicheng Wu;Fengqiang Li;F. Willomitzer;A. Veeraraghavan;O. Cossairt