Name
Abstract | ||
|---|---|---|
Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This paper introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TIP.2018.2884081 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Image coding,Sensors,Sparse matrices,Spatial resolution,Image reconstruction,Inverse problems | Iterative reconstruction,Computer vision,Spectral imaging,Image fusion,Sensor fusion,Pixel,Artificial intelligence,Inverse problem,Image resolution,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
28 | 5 | 1057-7149 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edwin Vargas | 1 | 1 | 3.07 |
| Oscar Espitia | 2 | 1 | 0.70 |
| Henry Arguello | 3 | 90 | 30.83 |
| Jean-Yves Tourneret | 4 | 1154 | 104.46 |