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CAREER: Co-Optimized Sensing and Reconstruction for Next-Generation Computational Cameras

职业：下一代计算相机的协同优化传感和重建

基本信息

批准号：
2048237
负责人：
Katherine Bouman
金额：
$ 56万
依托单位：
California Institute of Technology
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048237&HistoricalAwards=false
关键词：
CAREER Co Optimized Sensing Reconstruction

项目摘要

Imaging technology plays a critical role in advancing science. However, as science continues to push the boundaries of knowledge, traditional imaging sensors are reaching their limits. For example, traditional telescopes cannot be constructed large enough to resolve a black hole’s shadow; traditional microscopes are not able to see transparent cells, and traditional cameras cannot be used to study the inner core of a cloud due to scattering. Breaking these fundamental limits has only been possible through the emergence of computational cameras, which replace optics with computational algorithms; this new paradigm shift has enabled image formation processes that were previously infeasible for conventional optical imaging. The full potential of computational cameras is far from being realized; thus far they have primarily been identified and developed though human ingenuity. Consequently, computational imaging pipelines are often significantly under-optimized, and there is no doubt that many such “cameras” have yet to even be identified. Developing the next generation of computational cameras requires a fundamental shift away from relying on human intuition and overly simplified models in the design of imaging pipelines. This project aims to develop modern learning-based approaches to jointly optimize sensor and algorithm designs in computational camera pipelines in order to automatically discover new imaging strategies.The objective of this project is to develop a data-driven, generalizable learning framework that solves for a jointly optimized sensor design and reconstruction algorithm for computational imaging pipelines. The generalizable co-design framework will be developed to easily incorporate domain knowledge and respect physical constraints. In collaboration with domain-experts, the investigator will study the application of this framework to problems ranging from astronomical imaging to seismic imaging. The investigator will pursue fundamental work in four areas: 1) single-shot probabilistic co-design to optimize sensor design jointly with reconstruction methods, 2) online sequential probabilistic co-design for optimizing the next sensor measurement conditioned on previous measurements for a particular target, 3) co-design with a stochastically evolving target, and 4) co-design with a mismatched forward model. The investigator will make use of emerging computational techniques and machinery in machine learning, signal processing, optimization, applied math, and controls to efficiently co-optimize the computational imaging pipeline. This research will transform the way novel imaging pipelines are identified and developed, and will result in the development of new methods that will impact a wide array of important imaging problems, including astronomical, medical, seismic, and microscopic imaging.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.

成像技术在推动科学发展方面发挥着关键作用。然而，随着科学不断突破知识的边界，传统的成像传感器正在达到其极限。例如，传统的望远镜无法构造得足够大以分辨黑洞的阴影;传统的显微镜无法看到透明的细胞，传统的相机由于散射而无法用于研究云的内核。只有通过计算相机的出现，才有可能打破这些基本限制，计算相机用计算算法取代光学;这种新的范式转变使图像形成过程成为可能，而这在以前是不可行的。计算相机的全部潜力远未实现;迄今为止，它们主要是通过人类的聪明才智来识别和开发的。因此，计算成像流水线通常明显优化不足，并且毫无疑问，许多这样的“相机”甚至还没有被识别。开发下一代计算相机需要从根本上改变依赖人类直觉和过度简化的模型来设计成像管道。该项目旨在开发基于现代学习的方法，以联合优化计算相机管道中的传感器和算法设计，以自动发现新的成像策略。该项目的目标是开发一个数据驱动的，可推广的学习框架，以解决计算成像管道中的联合优化传感器设计和重建算法。将开发可推广的协同设计框架，以方便地将领域知识和尊重物理约束。与领域专家合作，研究人员将研究这一框架在从天文成像到地震成像等问题上的应用。研究人员将在四个领域开展基础工作：1）单次概率协同设计，以优化传感器设计与重建方法，2）在线顺序概率协同设计，以优化特定目标的下一个传感器测量，3）与随机演化目标的协同设计，以及4）与不匹配的前向模型的协同设计。研究人员将利用机器学习，信号处理，优化，应用数学和控制中新兴的计算技术和机器，以有效地共同优化计算成像管道。这项研究将改变识别和开发新型成像管道的方式，并将导致新方法的开发，这些方法将影响广泛的重要成像问题，包括天文，医学，地震和显微成像。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Discovering Structure From Corruption for Unsupervised Image Reconstruction

DOI：
10.1109/tci.2023.3325752
发表时间：
2023-04
期刊：
IEEE Transactions on Computational Imaging
影响因子：
5.4
作者：
Oscar Leong;Angela F. Gao;He Sun;K. Bouman
通讯作者：
Oscar Leong;Angela F. Gao;He Sun;K. Bouman

Score-Based Diffusion Models as Principled Priors for Inverse Imaging

DOI：
10.1109/iccv51070.2023.00965
发表时间：
2023-04
期刊：
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
影响因子：
0
作者：
Berthy T. Feng;Jamie Smith;Michael Rubinstein;Huiwen Chang;K. Bouman;W. T. Freeman
通讯作者：
Berthy T. Feng;Jamie Smith;Michael Rubinstein;Huiwen Chang;K. Bouman;W. T. Freeman

Inference of Black Hole Fluid-Dynamics from Sparse Interferometric Measurements