Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of IEEE International Conference on Image Processing 2016 |
| Publisher | IEEE |
| Publication date | 2016 |
| Pages | 2534-2538 |
| ISBN (Print) | 9781467399616 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | 23rd IEEE International Conference on Image Processing - Phoenix Convention Centre, Phoenix, AZ, United States Duration: 25 Sep 2016 → 28 Sep 2016 |
Conference
| Conference | 23rd IEEE International Conference on Image Processing |
|---|---|
| Location | Phoenix Convention Centre |
| Country | United States |
| City | Phoenix, AZ |
| Period | 25/09/2016 → 28/09/2016 |
Keywords
- Side information
- math.OC
- Compressive sensing
- Sparse signal
- n-`1 minimization
- Adaptive weights
Cite this
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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 proceeding › Article in proceedings › Research › peer-review
TY - GEN
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
Y1 - 2016
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.
KW - Side information
KW - math.OC
KW - Compressive sensing
KW - Sparse signal
KW - n-`1 minimization
KW - Adaptive weights
U2 - 10.1109/ICIP.2016.7532816
DO - 10.1109/ICIP.2016.7532816
M3 - Article in proceedings
SN - 9781467399616
SP - 2534
EP - 2538
BT - Proceedings of IEEE International Conference on Image Processing 2016
PB - IEEE
ER -