Name
Abstract | ||
|---|---|---|
Image fusion has been a hotspot in the area of image processing. How to extract and fuse the main and detailed information as accurately as possible from the source images into the single one is the key to resolving the above problem. Convolutional neural network (CNN) has been proved to be an effective tool to cope with many issues of image processing, such as image classification. In this paper, a novel image fusion method based on pulse-coupled neural network (PCNN) and CNN is proposed. CNN is used to obtain a series of convolution and linear layers which represent the high-frequency and low-frequency information, respectively. The traditional PCNN is improved to be responsible for selecting the coefficients of the sub-images. Experimental results indicate that the proposed method has obvious superiorities over the current main-streamed ones in terms of fusion performance and computational complexity. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67777-4_13 | INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017 |
Keywords | Field | DocType |
Image fusion, Pulse coupled neural network, Convolutional neural network, Time matrix | Pattern recognition,Image fusion,Convolutional neural network,Convolution,Computer science,Image processing,Artificial intelligence,Artificial neural network,Fuse (electrical),Contextual image classification,Computational complexity theory | Conference |
Volume | ISSN | Citations |
10559 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Kong | 1 | 34 | 9.67 |
| Wenzhun Huang | 2 | 76 | 6.08 |
| Yang Lei | 3 | 6 | 3.83 |