Reconstruction Algorithms in Compressive Sensing: An Overview
Abstract
The theory Compressive Sensing (CS) has provided a new acquisition strategy and recovery with good in the image processing area. This theory guarantees to recover a signal with high probability from a reduced sampling rate below the Nyquist-Shannon limit. The problem of recovering the original signal from the samples consists of solving an optimization problem. This article presents an overview of reconstruction algorithms for sparse signal recovery in CS, these algorithms may be broadly divided into six types. We have provided a comprehensive survey of the numerous reconstruction algorithms in CS aiming to achieve computational efficiency.
Type
Publication
In 11th edition of the Doctoral Symposium in Informatics Engineering, DSIE|16.
Date
February, 2016
