A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Optimizing Matrices For Compressed Sensing Using Existing Goodness Measures: Negative Results, And An Alternative
[article]
2017
arXiv
pre-print
The bound that arises out of sparse recovery analysis in compressed sensing involves input signal sparsity and some property of the sensing matrix. An effort has therefore been made in the literature to optimize sensing matrices for optimal recovery using this property. We discover, in the specific case of optimizing codes for the CACTI camera, that the popular method of mutual coherence minimization does not produce optimal results: codes designed to optimize effective dictionary coherence
arXiv:1707.03355v1
fatcat:hogj7l7eendkdd4zredxeqevie