Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
Title | Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Huijuan, Wang, Yong, Jiang, Xingmin, Ma |
Conference Name | 2019 28th Wireless and Optical Communications Conference (WOCC) |
Date Published | 25 July 2019 |
Publisher | IEEE |
ISBN Number | 978-1-7281-0660-1 |
Keywords | BEMD method, bi-dimensional empirical mode decomposition, bi-dimensional intrinsic mode functions, BIMF, compositionality, Computational efficiency, cyber physical systems, decomposition, details information, Empirical mode decomposition, fast bi-dimensional empirical mode decomposition, fixed neighborhood window method, Hermite interpolation, Hermite interpolation reconstruction method, Hilbert transforms, image decomposition, image fusion, Image reconstruction, interpolation, Market research, Metrics, Microsoft Windows, multisource image fusion decomposition, pubcrawl, source image features, surface interpolation, variable neighborhood window method, Wireless communication |
Abstract | Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. In the process of image decomposition, interpolation is needed and the upper and lower envelopes will be drawn. However, these interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite interpolation reconstruction method is used to replace the surface interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better. |
URL | https://ieeexplore.ieee.org/document/8770649 |
DOI | 10.1109/WOCC.2019.8770649 |
Citation Key | huijuan_fast_2019 |
- Hilbert transforms
- Wireless communication
- variable neighborhood window method
- surface interpolation
- source image features
- pubcrawl
- multisource image fusion decomposition
- microsoft windows
- Metrics
- Market research
- interpolation
- Image reconstruction
- image fusion
- image decomposition
- BEMD method
- Hermite interpolation reconstruction method
- Hermite interpolation
- fixed neighborhood window method
- fast bi-dimensional empirical mode decomposition
- Empirical mode decomposition
- details information
- decomposition
- cyber physical systems
- Computational efficiency
- Compositionality
- BIMF
- bi-dimensional intrinsic mode functions
- bi-dimensional empirical mode decomposition