Fusion of Greedy Pursuits for Compressed Sensing Signal Reconstruction

TL;DR

This paper introduces a fusion framework for Greedy Pursuits in compressed sensing, enhancing sparse signal recovery especially when prior distribution knowledge is unavailable or measurements are limited.

Contribution

A novel fusion approach for Greedy Pursuits and two new algorithms that improve sparse recovery performance in various measurement conditions.

Findings

Enhanced recovery in noisy measurements

Improved performance with limited measurements

Fusion schemes outperform individual pursuits

Abstract

Greedy Pursuits are very popular in Compressed Sensing for sparse signal recovery. Though many of the Greedy Pursuits possess elegant theoretical guarantees for performance, it is well known that their performance depends on the statistical distribution of the non-zero elements in the sparse signal. In practice, the distribution of the sparse signal may not be known a priori. It is also observed that performance of Greedy Pursuits degrades as the number of available measurements decreases from a threshold value which is method dependent. To improve the performance in these situations, we introduce a novel fusion framework for Greedy Pursuits and also propose two algorithms for sparse recovery. Through Monte Carlo simulations we show that the proposed schemes improve sparse signal recovery in clean as well as noisy measurement cases.