An edge enhancement method of radiographic weld image using bidimensional empirical mode decomposition

2016 ◽  
Author(s):  
Yalin Zhao ◽  
Jianmin Gao ◽  
Changying Dang ◽  
Yulin Xiao ◽  
Zhao Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Edge Enhancement ◽  
Mode Decomposition ◽  
Enhancement Method ◽  
Bidimensional Empirical Mode Decomposition

Medical Image Enhancement Method Based on Wavelet Transform and Mode Decomposition

2013 ◽  
Vol 389 ◽  
pp. 930-935 ◽  
Author(s):  
Ao Shuang Dong ◽  
Bin Bin Lou ◽  
Hui Yan Jiang ◽  
Qiang Tong ◽  
Guang Ming Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Empirical Mode Decomposition ◽  
Medical Image ◽  
Enhancement Effect ◽  
Mode Decomposition ◽  
Enhancement Method ◽  
Image Edge ◽  
Bidimensional Empirical Mode Decomposition ◽  
Medical Image Enhancement

Traditional medical image enhancement method has some disadvantages. They can not significantly improve the medical image edge, texture and detailed information. Besides the enhancement effect is susceptible to interference noise information. This paper proposed enhancement algorithms combining bidimensional empirical mode decomposition and the wavelet edge enhancement method. The first step is using the method of bidimensional empirical mode decomposition to process medical image, achieve image information with different frequency. And then our method using wavelet transform to enhance different frequency image edge, texture information. Through the comparison of proposed method with the existing method, it has been verified the proposed method has a better effect in the detail enhancement of medical images.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

An improved bidimensional empirical mode decomposition: A mean approach for fast decomposition

2014 ◽  
Vol 98 ◽  
pp. 344-358 ◽  
Author(s):  
Chin-Yu Chen ◽  
Shu-Mei Guo ◽  
Wei-sheng Chang ◽  
Jason Sheng-Hong Tsai ◽  
Kuo-Sheng Cheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition ◽  
Fast Decomposition
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