Name
Abstract | ||
|---|---|---|
•The difficulties that exist in conventional image fusion research are analyzed.•The advantages of deep learning (DL) techniques for image fusion are discussed.•A detailed review of existing DL-based image fusion methods is presented.•Several generic frameworks for DL-based image fusion are summarized and presented.•Some prospects for the future study of DL-based image fusion are put forward. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.inffus.2017.10.007 | Information Fusion |
Keywords | Field | DocType |
Image fusion,Deep learning,Convolutional neural network,Convolutional sparse representation,Stacked autoencoder | Digital photography,Image fusion,Medical imaging,Convolutional neural network,Sparse approximation,Composite image filter,Pixel,Artificial intelligence,Deep learning,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
42 | 1566-2535 | 38 |
PageRank | References | Authors |
0.93 | 80 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 492 | 30.80 |
| Xun Chen | 2 | 458 | 52.73 |
| Zengfu Wang | 3 | 1133 | 85.70 |
| Z. Jane Wang | 4 | 406 | 55.43 |
| Rabab K Ward | 5 | 1440 | 135.88 |
| Xuesong Wang | 6 | 267 | 42.23 |