Name
Abstract | ||
|---|---|---|
•Introduces Convolutional neural networks (CNNs) into the field of image fusion.•Discusses the feasibility and superiority of CNNs used for image fusion.•Proposes a state-of-the-art CNN-based multi-focus image fusion method.•Exhibits the potential of CNNs for other-type image fusion issues.•Puts forward some suggestions on the future study of CNN-based image fusion. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.inffus.2016.12.001 | Information Fusion |
Keywords | Field | DocType |
Image fusion,Multi-focus image fusion,Deep learning,Convolutional neural networks,Activity level measurement,Fusion rule | Image fusion,Computer science,Convolutional neural network,Fusion,Multi focus,Artificial intelligence,Deep learning,Wavelet,ENCODE,Computer vision,CLARITY,Pattern recognition,Machine learning | Journal |
Volume | Issue | ISSN |
36 | C | 1566-2535 |
Citations | PageRank | References |
94 | 1.92 | 35 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 492 | 30.80 |
| Xun Chen | 2 | 458 | 52.73 |
| hu peng | 3 | 106 | 2.58 |
| Zengfu Wang | 4 | 1133 | 85.70 |