Gradually Atom Pruning for Sparse Reconstruction and Extension to Correlated Sparsity

TL;DR

This paper introduces a novel sparse signal recovery algorithm that employs gradual atom pruning and extends to correlated sparsity, improving detection accuracy and leveraging correlation information for better reconstruction.

Contribution

The paper presents a new algorithm combining gradual atom pruning with correlation-aware extension, enhancing sparse reconstruction performance over existing methods.

Findings

Outperforms traditional compressed sensing algorithms in uncorrelated sparsity cases.

Effectively extends to correlated sparsity scenarios using correlation matrix.

Demonstrates improved accuracy and efficiency in signal recovery.

Abstract

We propose a new algorithm for recovery of sparse signals from their compressively sensed samples. The proposed algorithm benefits from the strategy of gradual movement to estimate the positions of non-zero samples of sparse signal. We decompose each sample of signal into two variables, namely "value" and "detector", by a weighted exponential function. We update these new variables using gradient descent method. Like the traditional compressed sensing algorithms, the first variable is used to solve the Least Absolute Shrinkage and Selection Operator (Lasso) problem. As a new strategy, the second variable participates in the regularization term of the Lasso (l1 norm) that gradually detects the non-zero elements. The presence of the second variable enables us to extend the corresponding vector of the first variable to matrix form. This makes possible use of the correlation matrix for a heuristic search in the case that there are correlations among the samples of signal. We compare the performance of the new algorithm with various algorithms for uncorrelated and correlated sparsity. The results indicate the efficiency of the proposed methods.