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

Huynh Van Luong, Jürgen Seiler, André Kaup, Søren Forchhammer

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

### Abstract

This work considers reconstructing a target signal in a context ofdistributed sparse sources. We propose an efficient reconstruction algorithmwith the aid of other given sources as multiple side information (SI). Theproposed algorithm takes advantage of compressive sensing (CS) with SI andadaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization.The proposed algorithm computes the adaptive weights in two levels, first eachindividual intra-SI and then inter-SI weights are iteratively updated at everyreconstructed iteration. This two-level optimization leads the proposedreconstruction algorithm with multiple SI using adaptive weights (RAMSIA) torobustly exploit the multiple SIs with different qualities. We experimentallyperform our algorithm on generated sparse signals and also correlated featurehistograms as multiview sparse sources from a multiview image database. Theresults show that RAMSIA significantly outperforms both classical CS and CSwith single SI, and RAMSIA with higher number of SIs gained more than the onewith smaller number of SIs.
Original language English Proceedings of IEEE International Conference on Image Processing 2016 IEEE 2016 2534-2538 9781467399616 https://doi.org/10.1109/ICIP.2016.7532816 Published - 2016 23rd IEEE International Conference on Image Processing - Phoenix Convention Centre, Phoenix, AZ, United StatesDuration: 25 Sep 2016 → 28 Sep 2016

### Conference

Conference 23rd IEEE International Conference on Image Processing Phoenix Convention Centre United States Phoenix, AZ 25/09/2016 → 28/09/2016

### Keywords

• Side information
• math.OC
• Compressive sensing
• Sparse signal
• n-1 minimization

### Cite this

Luong, H. V., Seiler, J., Kaup, A., & Forchhammer, S. (2016). Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources. In Proceedings of IEEE International Conference on Image Processing 2016 (pp. 2534-2538). IEEE. https://doi.org/10.1109/ICIP.2016.7532816
Luong, Huynh Van ; Seiler, Jürgen ; Kaup, André ; Forchhammer, Søren. / Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources. Proceedings of IEEE International Conference on Image Processing 2016. IEEE, 2016. pp. 2534-2538
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title = "Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources",
abstract = "This work considers reconstructing a target signal in a context ofdistributed sparse sources. We propose an efficient reconstruction algorithmwith the aid of other given sources as multiple side information (SI). Theproposed algorithm takes advantage of compressive sensing (CS) with SI andadaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization.The proposed algorithm computes the adaptive weights in two levels, first eachindividual intra-SI and then inter-SI weights are iteratively updated at everyreconstructed iteration. This two-level optimization leads the proposedreconstruction algorithm with multiple SI using adaptive weights (RAMSIA) torobustly exploit the multiple SIs with different qualities. We experimentallyperform our algorithm on generated sparse signals and also correlated featurehistograms as multiview sparse sources from a multiview image database. Theresults show that RAMSIA significantly outperforms both classical CS and CSwith single SI, and RAMSIA with higher number of SIs gained more than the onewith smaller number of SIs.",
keywords = "Side information, math.OC, Compressive sensing, Sparse signal, n-1 minimization, Adaptive weights",
author = "Luong, {Huynh Van} and J{\"u}rgen Seiler and Andr{\'e} Kaup and S{\o}ren Forchhammer",
year = "2016",
doi = "10.1109/ICIP.2016.7532816",
language = "English",
isbn = "9781467399616",
pages = "2534--2538",
booktitle = "Proceedings of IEEE International Conference on Image Processing 2016",
publisher = "IEEE",

}

Luong, HV, Seiler, J, Kaup, A & Forchhammer, S 2016, Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources. in Proceedings of IEEE International Conference on Image Processing 2016. IEEE, pp. 2534-2538, 23rd IEEE International Conference on Image Processing , Phoenix, AZ, United States, 25/09/2016. https://doi.org/10.1109/ICIP.2016.7532816

Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources. / Luong, Huynh Van; Seiler, Jürgen; Kaup, André; Forchhammer, Søren.

Proceedings of IEEE International Conference on Image Processing 2016. IEEE, 2016. p. 2534-2538.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources

AU - Luong, Huynh Van

AU - Seiler, Jürgen

AU - Kaup, André

AU - Forchhammer, Søren

PY - 2016

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N2 - This work considers reconstructing a target signal in a context ofdistributed sparse sources. We propose an efficient reconstruction algorithmwith the aid of other given sources as multiple side information (SI). Theproposed algorithm takes advantage of compressive sensing (CS) with SI andadaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization.The proposed algorithm computes the adaptive weights in two levels, first eachindividual intra-SI and then inter-SI weights are iteratively updated at everyreconstructed iteration. This two-level optimization leads the proposedreconstruction algorithm with multiple SI using adaptive weights (RAMSIA) torobustly exploit the multiple SIs with different qualities. We experimentallyperform our algorithm on generated sparse signals and also correlated featurehistograms as multiview sparse sources from a multiview image database. Theresults show that RAMSIA significantly outperforms both classical CS and CSwith single SI, and RAMSIA with higher number of SIs gained more than the onewith smaller number of SIs.

AB - This work considers reconstructing a target signal in a context ofdistributed sparse sources. We propose an efficient reconstruction algorithmwith the aid of other given sources as multiple side information (SI). Theproposed algorithm takes advantage of compressive sensing (CS) with SI andadaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization.The proposed algorithm computes the adaptive weights in two levels, first eachindividual intra-SI and then inter-SI weights are iteratively updated at everyreconstructed iteration. This two-level optimization leads the proposedreconstruction algorithm with multiple SI using adaptive weights (RAMSIA) torobustly exploit the multiple SIs with different qualities. We experimentallyperform our algorithm on generated sparse signals and also correlated featurehistograms as multiview sparse sources from a multiview image database. Theresults show that RAMSIA significantly outperforms both classical CS and CSwith single SI, and RAMSIA with higher number of SIs gained more than the onewith smaller number of SIs.

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KW - n-`1 minimization

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Luong HV, Seiler J, Kaup A, Forchhammer S. Sparse Signal Reconstruction with Multiple Side Information using Adaptive Weights for Multiview Sources. In Proceedings of IEEE International Conference on Image Processing 2016. IEEE. 2016. p. 2534-2538 https://doi.org/10.1109/ICIP.2016.7532816