compressed sensing and matrix completion

压缩感知和矩阵完成

• 批准号: 1805810
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1805810
• 关键词: compressed; sensing; matrix; completion

Nowadays we frequently encounter sensors generating high-dimensional data that make sampling by traditional ways difficult or even entirely impossible. Compressed sensing and matrixcompletion are techniques where data is acquired at the data information rate rather than what would be expected by its ambient dimension.The main goal of the project is to extend these techniques to higher dimensions and heterogeneous data.The first part of the project is concerned with sparse plus low rank models applied to hyperspectral imaging. Correlations in the hyperspectral images will be used to under-sample the tensor and then reconstruct it by optimising over the space of low tensor rank plus sparse innovations. We will consider a specific type of subsampling based on the use of mosaic filters. The aim is to understand what filter/image combination allow successful reconstruction, which couldpotentially lead to practical methods for compressive multispectral imaging, which is also of interest to Selex ES.The second part of the project will extend the aforementioned models to heterogeneous data types where disparate data of a common object is combined for reconstruction and/or classification.This project falls within the EPSRC Numerical Analysis research area.

如今，我们经常会遇到传感器生成高维数据，这使得传统方式的采样变得困难甚至完全不可能。压缩感知和矩阵补全是以数据信息速率而不是以其环境维度所期望的速率获取数据的技术，该项目的主要目标是将这些技术扩展到更高维度和异构数据，该项目的第一部分关注稀疏加低秩模型应用于高光谱成像。高光谱图像中的相关性将用于欠采样张量，然后通过优化低张量秩加上稀疏创新的空间来重建它。我们将考虑基于马赛克滤波器的使用的特定类型的子采样。目的是了解什么样的滤波器/图像组合可以成功重建，这可能会导致压缩多光谱成像的实用方法，该项目的第二部分将把上述模型扩展到异构数据类型，其中一个公共对象的不同数据被组合起来进行重建和/或这个项目属于EPSRC数值分析研究领域的福尔斯。

项目成果

A computational approach to hyperspectral imaging for long-range target identification

用于远程目标识别的高光谱成像计算方法

• DOI: 10.1117/12.2305306
• 发表时间: 2018
• 作者: Tanner J

Matrix Rigidity and the Ill-Posedness of Robust PCA and Matrix Completion

矩阵刚性以及鲁棒 PCA 和矩阵补全的不适定性

• DOI: 10.1137/18m1227846
• 发表时间: 2019
• 期刊: SIAM Journal on Mathematics of Data Science
• 影响因子: 3.6
• 作者: Tanner J

On low-rank plus sparse matrix sensing

关于低秩加稀疏矩阵感知

• 发表时间: 2021
• 作者: Vary S

Compressed sensing of low-rank plus sparse matrices

• DOI: 10.1016/j.acha.2023.01.008
• 发表时间: 2020-07
• 期刊: ArXiv
• 作者: Jared Tanner;Simon Vary

Multispectral Snapshot Demosaicing Via Non-Convex Matrix Completion

通过非凸矩阵补全进行多光谱快照去马赛克

• DOI: 10.1109/dsw.2019.8755561
• 发表时间: 2019
• 作者: Antonucci G