Name
Abstract | ||
|---|---|---|
Image fusion plays a significant role in various computer vision applications. However, designing an efficient image fusion technique is still a challenging task. In this paper, a novel deep belief networks based image fusion framework is proposed to improve the performance of the image fusion process further. We have initially, evaluated the fusion dataset by applying various feature extraction techniques. Thereafter, features selection techniques are applied to select potential features. Finally, the image fusion machine learning model is built by using a deep belief network model. Extensive experiments reveal that the proposed technique outperforms existing image fusion techniques. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10586-019-02999-x | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Image fusion,Deep belief networks,Medical images,Fuzzy rules | Journal | 23.0 |
Issue | ISSN | Citations |
2.0 | 1386-7857 | 2 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manjit Kaur | 1 | 23 | 8.41 |
| Dilbag Singh | 2 | 67 | 15.16 |