Name
Abstract | ||
|---|---|---|
Compressive sensing(CS) has inspired significant interest because of its compressive capability and lack of complexity on the sensor side. In this paper, we present a study of three sampling patterns and investigate their performance on CS reconstruction. We then propose a new image fusion algorithm in the compressive domain by using an improved sampling pattern. There are few studies regarding the applicability of CS to image fusion. The main purpose of this work is to explore the properties of compressive measurements through different sampling patterns and their potential use in image fusion. The study demonstrates that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the multiresolution (MR) domain. The simulations show that the proposed CS-based image fusion algorithm provides promising results. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/1816041.1816043 | Int. J. Comput. Math. |
Keywords | Field | DocType |
cs reconstruction,image fusion,compressive domain,different sampling pattern,improved sampling pattern,cs-based image fusion,proposed cs-based image fusion,compressive sensing,multiresolution image fusion,compressive measurement,compressive capability,new image fusion algorithm,compressed sensing | Computer vision,Image fusion,Computer science,Image fusion algorithm,Sampling (statistics),Artificial intelligence,Compressed sensing | Journal |
Volume | Issue | ISSN |
88 | 18 | 10290265 |
Citations | PageRank | References |
7 | 0.55 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Wan | 1 | 181 | 21.18 |
| Zengchang Qin | 2 | 439 | 45.46 |