Name
Abstract | ||
|---|---|---|
A blind compressive sensing algorithm is proposed to reconstruct hyperspectral images from spectrally-compressed measurements. The wavelength-dependent data are coded and then superposed, mapping the three-dimensional hyperspectral datacube to a two-dimensional image. The inversion algorithm learns a dictionary in situ from the measurements via globallocal shrinkage priors. By using RGB images as side information of the compressive sensing system, the proposed approach is extended to learn a coupled dictionary from the joint dataset of the compressed measurements and the corresponding RGB images, to improve reconstruction quality. A prototype camera is built using a liquid-crystal-on-silicon modulator. Experimental reconstructions of hyperspectral datacubes from both simulated and real compressed measurements demonstrate the efficacy of the proposed inversion algorithm, the feasibility of the camera and the benefit of side information. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTSP.2015.2411575 | Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Compressive sensing, hyperspectral image, side information, Bayesian shrinkage, dictionary learning, blind compressive sensing, computational photography, coded aperture snapshot spectral imaging (CASSI), spatial light modulation | Iterative reconstruction,Computer vision,Full spectral imaging,Computer science,Hyperspectral imaging,Artificial intelligence,RGB color model,Prior probability,Detector,Compressed sensing,Data cube | Journal |
Volume | Issue | ISSN |
PP | 99 | 1932-4553 |
Citations | PageRank | References |
23 | 0.78 | 26 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Yuan | 1 | 383 | 27.60 |
| Tsung-Han Tsai | 2 | 23 | 0.78 |
| Ruoyu Zhu | 3 | 23 | 0.78 |
| Patrick Llull | 4 | 150 | 5.37 |
| David J. Brady | 5 | 338 | 21.97 |
| L. Carin | 6 | 4603 | 339.36 |