CIF: Small: Collaborative Research: Signal Processing for Nonlinear Diffractive Imaging: Acquisition, Reconstruction, and Applications

CIF：小型：协作研究：非线性衍射成像的信号处理：采集、重建和应用

• 批准号: 1813848
• 负责人: Lei Tian
• 金额: $ 25.07万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813848&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Signal

There is a growing need in biomedical research to observe biological structure and processes on the length scales smaller than 100nm. Conventional optical systems cannot effectively provide such information, however, due to the infamous diffraction limit. The formulation of the diffraction limit fundamentally relies on the presumed linearity in the interaction between the illuminating light and the object. The goal of this project is to overcome the diffraction limit by exploiting nonlinearities from a modern signal processing perspective. This project will lay out theoretical, algorithmic, and instrumentation foundation for nonlinear diffractive imaging. The outcome of this project can open up many possibilities of high-resolution imaging in highly scattering media that are ubiquitous in science and medicine, including histology, cytometry, brain mapping, and drug discovery. The proposed activities will also promote education through the training of students in a wide range of topics within signal processing, computational imaging, and optics, and through related curriculum development efforts where the basic themes of the proposed research will be incorporated in courses taught by the investigators.The focus of this research is to develop computational imaging methods that fully harness the information encoded in two types of nonlinearities: object-light interactions and prior assumptions on the objects. Concretely, the work will focus on: (a) efficient schemes for data acquisition, (b) scalable algorithms for 3D reconstruction, and (c) two novel application platforms for validation. Results from this research will lead to a single, holistic signal processing framework that can maximally extract the information encoded in nonlinear measurements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

生物医学研究的需求日益增长，以观察到小于100nm的长度尺度上的生物结构和过程。但是，由于臭名昭著的衍射极限，常规的光学系统无法有效地提供此类信息。衍射极限的配方从根本上依赖于照明光与物体之间相互作用中的线性。该项目的目的是通过从现代信号处理的角度利用非线性来克服衍射极限。该项目将列出非线性衍射成像的理论，算法和仪器基础。该项目的结果可以在高度散射培养基中打开许多高分辨率成像的可能性，这些介质在科学和医学中无处不在，包括组织学，细胞仪，脑图和药物发现。所提出的活动还将通过在信号处理，计算成像和光学范围内的广泛主题的培训以及相关的课程开发工作中培训教育，在这些课程中，将纳入研究人员的课程中。该研究的重点是该研究的重点是在两种类型的对象和对象中完全培养信息的计算成像方法：对象和对象：对象：对象：对象：对象：对象：对象：对象：对象进行对象。具体而言，工作将重点放在：（a）用于数据采集的有效方案，（b）3D重建的可扩展算法，以及（c）两个用于验证的新型应用程序平台。这项研究的结果将导致一个整体的信号处理框架，该框架可以最大程度地提取非线性测量中编码的信息。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来审查标准的评估值得的支持。

项目成果

SIMBA: Scalable Inversion in Optical Tomography Using Deep Denoising Priors

• DOI: 10.1109/jstsp.2020.2999820
• 发表时间: 2019-11
• 期刊: IEEE Journal of Selected Topics in Signal Processing
• 影响因子: 7.5
• 作者: Zihui Wu;Yu Sun;Alex Matlock;Jiaming Liu;L. Tian;U. Kamilov

Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network

• DOI: 10.1364/oe.411291
• 发表时间: 2021-01-18
• 期刊: OPTICS EXPRESS
• 影响因子: 3.8
• 作者: Li, Yunzhe;Cheng, Shiyi;Tian, Lei
• 通讯作者: Tian, Lei

Acousto-optic ptychography

• DOI: 10.1364/optica.424828
• 发表时间: 2021-06-20
• 期刊: OPTICA
• 影响因子: 10.4
• 作者: Rosenfeld, Moriya;Weinberg, Gil;Katz, Ori
• 通讯作者: Katz, Ori

Model and learning-based computational 3D phase microscopy with intensity diffraction tomography

• DOI: 10.23919/eusipco47968.2020.9287407
• 发表时间: 2021-01
• 期刊: 2020 28th European Signal Processing Conference (EUSIPCO)
• 作者: Alex Matlock;Yujia Xue;Yunzhe Li;Shiyi Cheng;Waleed Tahir;L. Tian

Holographic particle localization under multiple scattering

多重散射下的全息粒子定位

• DOI: 10.1117/1.ap.1.3.036003
• 发表时间: 2019
• 期刊: Advanced Photonics
• 影响因子: 17.3
• 作者: Tahir, Waleed;Kamilov, Ulugbek S.;Tian, Lei
• 通讯作者: Tian, Lei