CAREER: Generative Physical Modeling for Computational Imaging Systems

职业：计算成像系统的生成物理建模

• 批准号: 2239687
• 负责人: Jonathan Tamir
• 金额: $ 57.67万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239687&HistoricalAwards=false
• 关键词: CAREER; Generative; Physical; Modeling; Computational

Imaging devices, from microscopes to medical-imaging scanners, have transformed science and diagnostic medicine by providing safe and noninvasive techniques for observing the environment and seeing inside the body. However, imaging-system design choices are often based on idealized operating conditions, resulting in highly promising "benchtop demonstrations" that quickly degrade when deployed outside of a controlled laboratory environment. This project aims to develop a framework for robust computational-imaging system design, where the data acquisition and data processing are jointly designed in tandem to address the mismatch between the idealized performance of physical systems and their real-world behavior. The research aims to enable reliable imaging in dynamically evolving clinical and scientific-research settings, for example by reducing acquisition times, imaging moving objects, and compensating for system imperfections. The project also involves the dissemination of results through tutorials, webinars, and reproducible code, as well as community-outreach initiatives based on hands-on interactive demos of computational-imaging systems with the goal of broadening participation in engineering and science. The objective of this project is to develop a machine-learning framework for robust computational-imaging system design with principled methods and theoretical performance guarantees. Central to the approach is the separation between modeling the physical system, which is governed by established imaging physics, and modeling the statistical prior knowledge, which is learned from imaging data. The project involves four research thrusts, each intended to tackle a specific problem foundational to computational-imaging system robustness and reliability: 1) adapting to dynamically changing operating conditions; 2) accounting for motion during image acquisition; 3) learning directly from noisy, sub-sampled measurements; and 4) improving resiliency to system imperfections. The project is founded on a fruitful synthesis between imaging physics, signal processing, optimization, and machine learning. Image acquisition and recovery will be formalized using newly developed deep generative physical models instead of poorly understood and non-generalizable black-box deep-learning methods. In addition to impacting applications including microscopic, medical, and automotive imaging, the work will also inform the design of non-imaging systems, such as in wireless communications, where similar challenges exist surrounding the deployment of deep-learning-based algorithms.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.

从显微镜到医学成像扫描仪的成像设备通过提供安全且无创的技术来观察环境并看到体内的安全技术，从而改变了科学和诊断医学。但是，成像系统设计选择通常基于理想化的操作条件，从而导致高度有希望的“台式演示”，当部署在受控实验室环境外时，它们很快就会降低。该项目旨在为健壮的计算成像系统设计开发一个框架，在该框架中，数据采集和数据处理的共同设计旨在解决物理系统的理想化性能与其现实世界行为之间的不匹配。该研究旨在在动态发展的临床和科学研究环境中启用可靠的成像，例如，减少采集时间，成像移动对象并补偿系统缺陷。该项目还涉及通过教程，网络研讨会和可再现的代码以及基于计算成像系统的动手交互式演示的社区隔离计划来传播结果。该项目的目的是开发一个机器学习框架，用于具有原则性方法和理论性能保证的稳健计算系统设计。该方法的核心是建模物理系统之间的分离，该物理系统由已建立的成像物理学控制，并对统计的先验知识进行建模，这是从成像数据中学到的。该项目涉及四个研究推力，每个研究都旨在解决计算成像系统的基础稳健性和可靠性的基础：1）适应动态变化的操作条件； 2）在图像获取过程中考虑运动； 3）直接从嘈杂的亚采样测量中学习； 4）提高对系统缺陷的弹性。该项目建立在成像物理，信号处理，优化和机器学习之间的富有成果的综合上。图像采集和恢复将使用新开发的深层生成物理模型进行形式化，而不是理解和不可替代的黑盒深度学习方法。除了影响包括显微镜，医学和汽车成像在内的应用应用程序外，这项工作还将为非成像系统的设计提供信息，例如在无线通信中，在无线通信中存在类似的挑战，围绕着基于深度学习的算法的部署，这项奖项奖均反映了NSF的法定任务，并通过评估范围来反映了对基础的支持，并通过评估了范围的范围。