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Optimal spectral reconstructions from deterministic and stochastic sampling geometries using compressive sensing and spectral statistical models

机译：使用压缩感测和光谱统计模型从确定性和随机采样几何结构中优化光谱重建

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作者
Jeromin, Oliver Max;
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作者单位

The University of New Mexico;

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授予单位 The University of New Mexico;
学科
学位 Ph.D.
年度 2009
页码 227 p.
总页数 227
原文格式 PDF
正文语种 eng
中图分类
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专利

1. Optimal compressed sensing reconstructions of fMRI using 2D deterministic and stochastic sampling geometries [J] . Oliver Jeromin, Marios S Pattichis, Vince D Calhoun BioMedical Engineering OnLine . 2012,第1期

机译：使用2D确定性和随机采样几何结构的fMRI最佳压缩感知重建
2. Exploring error of linear mixed model for hyperspectral image reconstruction from spectral compressive sensing [J] . Wang Zhongliang, He Mi, Ye Zhen, Journal of Applied Remote Sensing . 2019,第3期

机译：光谱压缩检测探讨高光谱图像重建线性混合模型的误差
3. Line Spectral Estimation Based on Compressed Sensing with Deterministic Sub-Nyquist Sampling [J] . Huang Shan, Sun Hong, Zhang Haijian, Circuits, systems, and signal processing . 2018,第4期

机译：确定性亚奈奎斯特采样的基于压缩感知的线谱估计
4. Sparse Reconstruction of Compressed Sensing Multi-spectral Data using Cross-Spectral Multi-layered Conditional Random Field Model [C] . Edward Li, Mohammad Javad Shafiee, Farnoud Kazemzadeh, Conference on applications of digital image processing . 2015

机译：跨谱多层条件随机场模型的压缩感知多谱数据稀疏重建
5. Analysis of Compressive Sensing for Hyperspectral Remote Sensing Applications. [D] . Busuioceanu, Maria. 2013

机译：高光谱遥感应用中的压缩传感分析。
6. Optimal compressed sensing reconstructions of fMRI using 2D deterministic and stochastic sampling geometries [O] . Oliver Jeromin, Marios S Pattichis, Vince D Calhoun 2012

机译：使用2D确定性和随机采样几何结构的fMRI最佳压缩感知重建
7. Optimal compressed sensing reconstructions of fMRI using 2D deterministic and stochastic sampling geometries [O] . 2012

机译：使用2D确定性和随机采样几何结构的fMRI最佳压缩感知重建
8. Spectral Equivalence of Deterministic and Stochastic Models for Time Series with a Periodic Component. [R] . O'Connor, M. J. 1982

机译：具有周期分量的时间序列确定性和随机模型的谱等价性。

Optimal spectral reconstructions from deterministic and stochastic sampling geometries using compressive sensing and spectral statistical models

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