Improving empirical mode decomposition using support vector machines for multifocus image fusion
文献类型：SCI/SSCI论文
| 作者 | Tian J. |
| 发表日期 | 2008 |
| 关键词 | multifocus image fusion empirical mode decomposition 'a-trous' wavelet transform support vector machines |
| 英文摘要 | Empirical mode decomposition ( EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines ( SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fusing multifocus images. Experimental results show that the proposed method is superior to the fusion methods based on a-trous wavelet transform ( AWT) and EMD in terms of quantitative analyses by Root Mean Squared Error ( RMSE) and Mutual Information ( MI). |
| 出处 | Sensors |
| 卷 | 8 |
| 期 | 4 |
| 页 | 2500-2508 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 1424-8220 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24227]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Tian J.. Improving empirical mode decomposition using support vector machines for multifocus image fusion. 2008. |
入库方式： OAI收割
来源：地理科学与资源研究所
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