CAREER: Neural Computational Imaging - A Path Towards Seeing Through Scattering

职业：神经计算成像——透视散射的途径

• 批准号: 2339616
• 负责人: Christopher Metzler
• 金额: $ 64.95万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339616&HistoricalAwards=false
• 关键词: CAREER; Neural; Computational; Imaging; Path

Imaging through scattering is arguably the most important open problem in applied optics. If one could overcome scattering and other optical aberrations, one could (a) see through tissue to observe "biology in action" at cellular scale; (b) see through fog, smoke, and inclement weather to safely navigate in adverse conditions; (c) see through the atmosphere to allow ground-based telescopes to outperform James Webb for a fraction of the cost; and (d) see through thin fiber bundles to enable minimally invasive endoscopy. The physics of scattering is now relatively well-understood and the obstacles to effectively image through scattering are primarily computational in nature: Existing algorithms cannot efficiently disentangle measurements of scattered photons to recover the underlying structure of a time-varying three-dimensional scene hidden behind a scattering medium. This project develops a collection of signal processing and machine learning innovations to broadly address this challenge. This project also develops a portable imaging-through-scattering demonstrator that will be used to engage high-school and undergraduate students in STEM. It also supports the development of a new hands-on cross-disciplinary undergraduate computational-imaging course that will improve US workforce development.The overarching goal of this project is to develop a "neural computational imaging" framework capable of solving challenging, high-dimensional, non-stationary computational-imaging problems. The framework consists of three key innovations: First, functional neural signal representations will be used to capture and exploit a signal's low-dimensional structure and temporal regularity without explicit models. Second, neural forward operators will provide an interpretable, computationally efficient, and easy-to-calibrate approach to model non-stationary imaging inverse problems. Finally, self-supervised learning will be used to extract data-driven priors in imaging applications where ground-truth images/signals are not available. Collectively, these innovations are expected to provide breakthrough imaging-through-scattering capabilities for use in national defense, remote sensing, robotics, astronomy, microscopy, endoscopy, pathology, and other applications.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.

通过散射成像可以说是应用光学中最重要的开放问题。如果人们能够克服散射和其他光学畸变，人们就可以（a）透过组织观察细胞尺度的“生物活动”;（B）透过雾、烟和恶劣天气观察，在不利条件下安全导航;（c）透过大气层观察，使地面望远镜的性能优于詹姆斯·韦伯（James WebB），而成本却只有后者的一小部分;和（d）透视细纤维束以实现最小侵入性内窥镜检查。散射的物理学现在相对较好地理解，通过散射有效成像的障碍主要是计算性的：现有算法不能有效地解开散射光子的测量，以恢复隐藏在散射介质后面的时变三维场景的底层结构。该项目开发了一系列信号处理和机器学习创新，以广泛应对这一挑战。该项目还开发了一种便携式散射成像演示器，将用于吸引高中和本科生参与STEM。它还支持开发一个新的动手跨学科的本科计算成像课程，这将改善美国劳动力的发展。该项目的总体目标是开发一个“神经计算成像”框架，能够解决具有挑战性的，高维的，非稳态的计算成像问题。该框架包括三个关键的创新：第一，功能神经信号表示将用于捕获和利用信号的低维结构和时间规律性，而无需显式模型。其次，神经前向算子将提供一种可解释的，计算效率高，易于校准的方法来模拟非平稳成像逆问题。最后，自我监督学习将用于在无法获得地面实况图像/信号的成像应用中提取数据驱动的先验。总的来说，这些创新有望为国防、遥感、机器人、天文学、显微镜、内窥镜检查、病理学和其他应用提供突破性的散射成像能力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。