DeepAI AI Chat
Log In Sign Up

Recovery of Binary Sparse Signals from Structured Biased Measurements

06/26/2020
by   Sandra Keiper, et al.
0

In this paper we study the reconstruction of binary sparse signals from partial random circulant measurements. We show that the reconstruction via the least-squares algorithm is as good as the reconstruction via the usually used program basis pursuit. We further show that we need as many measurements to recover an s-sparse signal x_0∈ℝ^N as we need to recover a dense signal, more-precisely an N-s-sparse signal x_0∈ℝ^N. We further establish stability with respect to noisy measurements.

READ FULL TEXT

page 12

page 13

11/06/2018

A New Analysis for Support Recovery with Block Orthogonal Matching Pursuit

Compressed Sensing (CS) is a signal processing technique which can accur...
03/15/2023

Learning to Reconstruct Signals From Binary Measurements

Recent advances in unsupervised learning have highlighted the possibilit...
05/05/2012

Rakeness in the design of Analog-to-Information Conversion of Sparse and Localized Signals

Design of Random Modulation Pre-Integration systems based on the restric...
04/07/2022

Gravitationally Lensed Black Hole Emission Tomography

Measurements from the Event Horizon Telescope enabled the visualization ...
12/02/2016

Active Search for Sparse Signals with Region Sensing

Autonomous systems can be used to search for sparse signals in a large s...
11/04/2022

Embracing Off-the-Grid Samples

Many empirical studies suggest that samples of continuous-time signals t...
12/06/2017

Tomographic Reconstruction using Global Statistical Prior

Recent research in tomographic reconstruction is motivated by the need t...