Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6959659 | Signal Processing | 2015 | 14 Pages |
Abstract
It has been shown that iterative re-weighted strategies will often improve the performance of many sparse reconstruction algorithms. However, these strategies are algorithm dependent and cannot be easily extended for an arbitrary sparse reconstruction algorithm. In this paper, we propose a general iterative framework and a novel algorithm which iteratively enhance the performance of any given arbitrary sparse reconstruction algorithm. We theoretically analyze the proposed method using restricted isometry property and derive sufficient conditions for convergence and performance improvement. We also evaluate the performance of the proposed method using numerical experiments with both synthetic and real-world data.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Sooraj K. Ambat, K.V.S. Hari,