Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources

TL;DR

This paper introduces RAMSIA, an adaptive weighted algorithm for reconstructing sparse signals using multiple side information sources, significantly improving performance over classical methods in multiview sparse source scenarios.

Contribution

The paper presents a novel two-level adaptive weighting scheme for multi-side information in compressive sensing, enhancing reconstruction robustness and accuracy.

Findings

RAMSIA outperforms classical CS and single SI methods.

Using more side information sources improves reconstruction quality.

Experimental results on multiview data validate the effectiveness of RAMSIA.

Abstract

This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization. The proposed algorithm computes the adaptive weights in two levels, first each individual intra-SI and then inter-SI weights are iteratively updated at every reconstructed iteration. This two-level optimization leads the proposed reconstruction algorithm with multiple SI using adaptive weights (RAMSIA) to robustly exploit the multiple SIs with different qualities. We experimentally perform our algorithm on generated sparse signals and also correlated feature histograms as multiview sparse sources from a multiview image database. The results show that RAMSIA significantly outperforms both classical CS and CS with single SI, and RAMSIA with higher number of SIs gained more than the one with smaller number of SIs.