2015/04/06 by K. G. Deepa, G., Deepa K., Sooraj K. Ambat +3
Engineering · Medicine · #Advanced MRI Techniques and Applications #FOS: Computer and information sciences #Information Theory (cs.IT) #Methodology (stat.ME) #Photoacoustic and Ultrasonic Imaging #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1504.01705
openalex publication_date 2015/04/06 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
We consider the recovery of sparse signals that share a common support from multiple measurement vectors. The performance of several algorithms developed for this task depends on parameters like dimension of the sparse signal, dimension of measurement vector, sparsity level, measurement noise. We propose a fusion framework, where several multiple measurement vector reconstruction algorithms participate and the final signal estimate is obtained by combining the signal estimates of the participating algorithms. We present the conditions for achieving a better reconstruction performance than the participating algorithms. Numerical simulations demonstrate that the proposed fusion algorithm often performs better than the participating algorithms.