Gradient constrained bi-dimensional empirical mode decomposition and its application
- Resource Type
- Conference
- Authors
- Xu, Xiaogang; Chong, Yuan; Jin, Xin; Wang, Jianguo; Xu, Guanlei; Qin, Xujia
- Source
- 2015 8th International Congress on Image and Signal Processing (CISP) Image and Signal Processing (CISP), 2015 8th International Congress on. :929-933 Oct, 2015
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Image fusion
Empirical mode decomposition
Image edge detection
Image enhancement
Algorithm design and analysis
Wavelet transforms
gradient
bemd
image enhance
image fusion
- Language
In order to avoid the shortcomings of the capacity of getting the image details through the traditional bi-dimensional empirical mode decomposition (BEMD), an improved bi-dimensional Empirical Mode Decomposition method is proposed based on the gradient and local extrema. It can gain the high frequency edge information of the image by the gradient's strong mining capacity to the image detail information. In addition, a new fusion strategy is realized by using non negative matrix factorization method as the fusion rule. The result shows that this method owns better detail capture capability than traditional enhancement and fusion algorithm.