Name
Abstract | ||
|---|---|---|
Compressed sensing (CS) is a new signal processing theory that provides an insight into signal processing. The CS theory has numerous potential applications in various fields, such as image processing, astronomical data analysis, analog-to-information, medical imaging, and remote sensing (RS) imagery. The CS theory is applied to RS video imagery. An RS video based on a compressed sensing (RS-VCS) framework with correlation estimation measurement is proposed, along with a block measurement correlation model and corresponding reconstruction. The linearized Bregman algorithm is used to solve the reconstruction model, and the performance of the RS-VCS framework is simulated numerically. (C) 2014 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1117/1.JEI.23.6.063007 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
compressed sensing,remote sensing video,correlation estimation | Computer vision,Signal processing,Pattern recognition,Medical imaging,Computer science,Remote sensing,Image processing,Correlation,Sampling (statistics),Artificial intelligence,Compressed sensing | Journal |
Volume | Issue | ISSN |
23 | 6 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sheng-liang Li | 1 | 0 | 0.34 |
| kun liu | 2 | 1 | 2.07 |
| Li Zhang | 3 | 141 | 20.37 |
| Jie Wang | 4 | 0 | 1.01 |
| Zhizhou Zhang | 5 | 12 | 3.37 |
| Dapeng Han | 6 | 0 | 0.34 |