van Luong H, Seiler J, Kaup A, Forchhammer S (2016)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2016
City/Town: Phoenix, AZ, USA
Pages Range: 2534-2538
Conference Proceedings Title: IEEE Int. Conf. on Image Processing (ICIP)
ISBN: 978-1-4673-9961-6
DOI: 10.1109/ICIP.2016.7532816
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-ℓ 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.
APA:
van Luong, H., Seiler, J., Kaup, A., & Forchhammer, S. (2016). Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources. In IEEE Int. Conf. on Image Processing (ICIP) (pp. 2534-2538). Phoenix, AZ, US: Phoenix, AZ, USA.
MLA:
van Luong, Huynh, et al. "Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources." Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ Phoenix, AZ, USA, 2016. 2534-2538.
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