# Covariance Estimation From Compressive Data Partitions Using a Projected Gradient-Based Algorithm

@article{Monsalve2021CovarianceEF, title={Covariance Estimation From Compressive Data Partitions Using a Projected Gradient-Based Algorithm}, author={Jonathan Monsalve and Juan Marcos Ram{\'i}rez and I{\~n}aki Esnaola and Henry Arguello}, journal={IEEE Transactions on Image Processing}, year={2021}, volume={31}, pages={4817-4827} }

Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently, these methods have been used in various image processing and communications applications, including denoising, spectrum sensing, and compression. Notice that estimating the covariance matrix from compressive samples leads to ill-posed minimizations with severe performance loss at high compression rates. In this…

## 3 Citations

### Compressive Covariance Sensing-Based Power Spectrum Estimation of Real-Valued Signals Subject to Sub-Nyquist Sampling

- Computer Science
- 2021

An estimate of the power spectrum of a real-valued wide-sense stationary autoregressive signal is computed from sub-Nyquist or compressed measurements in additive white Gaussian noise using the concepts of compressive covariance sensing and Blackman-Tukey nonparametric spectrum estimation.

### Hierarchical Compressed Subspace Clustering Of Infrared Single-Pixel Measurements

- Computer Science2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
- 2022

This paper proposes a hierarchical approach to design the sensing matrix of the SPC, such that the pixel clustering task can be performed directly using the compressed infrared SPC measurements without a previous reconstruction step.

### Cocosvi: Single Snapshot Compressive Spectral Video Via Covariance Matrix Estimation

- Computer Science2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
- 2022

A snapshot spectral video imager based on the compressive covariance sampling (CCS) theory, named CoCoS-Vi, and a low-rank optimization problem that exploits the covariance matrix (CM) spectrotemporal correlation to improve the reconstruction accuracy is proposed.

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