CRII: CIF: Coordinate-based Neural Networks for Inverse Problems in Computational Imaging

CRII：CIF：计算成像逆问题的基于坐标的神经网络

基本信息

批准号：
2153371
负责人：
Gregory Ongie
金额：
$ 17.32万
依托单位：
Marquette University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153371&HistoricalAwards=false
关键词：
CRII CIF Coordinate based Neural

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Modern image processing relies heavily on pixel representations of images. However, in the context of computational imaging, working with pixel representations requires making approximations which may bias the image-estimation process and therefore negatively impact image quality as a result. This project seeks to overcome these limitations through a new type of image representation based on neural networks that is more compatible with standard computational imaging models. The main research aims are to develop a theory quantifying the expected accuracy of computational imaging techniques using these neural-network image representations, and to derive efficient estimation algorithms applicable to practical high-resolution imaging problems. This research has foreseeable applications in all areas of science and engineering where computational imaging plays a critical role, including medical imaging and diagnostics, security screening, seismic imaging, and environmental monitoring.This project investigates the use of a class of neural networks, known as Coordinate-Based Neural Networks (CBNNs), for image-reconstruction problems in computational imaging. A CBNN represents an image as a continuous domain function mapping spatial coordinates to image intensities. Because common imaging-forward models, such as continuous Radon or Fourier transforms, can be implemented more accurately for CBNNs, they have the potential to improve the accuracy of model-based iterative reconstruction techniques. Specific objectives of this project include (1) developing a sampling theory for the unique identifiability of CBNNs from a finite set of linear projection measurements and recovery guarantees for the associated non-convex optimization problem, and (2) developing efficient algorithms for accelerated training of CBNNs that scale to practical imaging scenarios. The theory and algorithms will be demonstrated on large-scale applications with real data, including compressed sensing magnetic-resonance imaging and low-dose/sparse-view computerized tomography.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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。现代图像处理在很大程度上取决于图像的像素表示。但是，在计算成像的背景下，使用像素表示需要进行近似，以使图像估计过程偏差，从而对图像质量产生负面影响。该项目旨在通过基于与标准计算成像模型更兼容的神经网络的新型图像表示来克服这些局限性。主要研究目的是开发一种理论，使用这些神经网络图像表示量化计算成像技术的预期准确性，并得出适用于实用高分辨率成像问题的有效估计算法。这项研究在科学和工程的所有领域都有可预见的应用，其中计算成像起着至关重要的作用，包括医学成像和诊断，安全筛查，地震成像和环境监测。该项目研究了一类神经网络的使用，称为基于坐标的神经网络（CBNNS），用于计算成像中的图像构造问题。 CBNN将图像表示为连续域函数映射空间坐标到图像强度。因为可以更准确地实现CBNN的常见成像前向模型，例如连续ra或傅立叶变换，因此它们具有提高基于模型的迭代重建技术的准确性。该项目的具体目标包括（1）为CBNN的唯一可识别性开发了从有限的线性投影测量值和相关非convex优化问题的恢复保证的唯一可识别性，以及（2）开发有效的算法，以加速对CBNN的加速培训，以扩展到实践成像场景。该理论和算法将在具有真实数据的大规模应用中进行证明，包括压缩感测磁性成像和低剂量/稀疏视图计算机断层扫描。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力和更广泛影响的评估来通过评估来获得支持的。