Image fusion combining FABEMD with improved saliency detection

被引：1
|
作者：
An Y. [1 ]
Fan X. [1 ]
Chen L. [1 ]
Liu P. [1 ]
机构：
[1] School of Information Science and Technology, Northwest University, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 02期
关键词：
Fast and adaptive bidimensional empirical mode decomposition (FABEMD); Guided filter; Image fusion; Saliency detection;
D O I：
10.3969/j.issn.1001-506X.2020.02.06
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the significant targets are not prominent, the contrast is low, and there are many artifacts in infrared and visible image fusion, an image fusion algorithm combining fast and adaptive bidimensional empirical mode decomposition (FABMED) with improved visual saliency detection is proposed. First, the multi-scale decomposition of infrared and visible images is performed by FABEMD to obtain the corresponding base layer and detail layers. A dim suppression improvement is then performed on the maximum symmetric surround saliency detection, which is used for the fusion of the base layer. Combined with the improved saliency detection and guided filter, the detail layers are fused. To this end, the inverse FABEMD transform on each fusion sub-image is performed to reconstruct the fused image. Compared with other typical fusion algorithms, the simulation experiments verify the effectiveness of the proposed algorithm. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：292 / 300
页数：8
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