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Abstract: Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set of projected measurements.
Nov 19, 2007
(PDF) Optimized Sparse Projections for Compressive Sensing
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Sep 13, 2024 · We consider designing a sparse sensing matrix which contains few non-zero entries per row for compressive sensing (CS) systems.
Sep 19, 2017 · We consider designing a robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a dense ...
Apr 26, 2018 · Compressed Sensing (CS) is a new data acquisition theory based on the existence of a sparse representation of a signal and a projected ...
In this talk we focus on this issue, offering a way to design these projections to yield better CS performance. Page 3. Optimized Projection. Directions for.
Abstract: Sparse signals can be sensed with a reduced number of random projections and then reconstructed if compressive sensing (CS) is employed.
Optimized Projections for Compressed Sensing - ACM Digital Library
dl.acm.org › doi › tsp.2007.900760
Compressed sensing (CS) offers a joint compression and sensing processes, based on the existence of a sparse representation of the treated signal and a set ...
A robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a base matrix with fast implementation.
Aug 13, 2015 · Compressed Sensing (CS) is a novel technique for simultaneous signal sampling and compression based on the existence of a sparse representation ...
This document proposes a framework for optimizing sparse sensing matrices for compressive sensing. It unifies previous approaches that minimize mutual coherence ...