Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources
This work addresses signal reconstruction in distributed sparse sources, such as multiview images, but is incremental as it builds on existing compressive sensing with side information techniques.
The paper tackles the problem of reconstructing a target sparse signal using multiple side information sources, proposing an algorithm called RAMSIA that uses adaptive weights in a two-level optimization. The results show that RAMSIA significantly outperforms classical compressive sensing and single side information methods, with performance gains increasing with more side information sources.
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.