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Sensors 2008, 8(4), 2500-2508; doi:10.3390/s8042500
Article

Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion

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Received: 3 March 2008; Accepted: 31 March 2008 / Published: 8 April 2008
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Abstract: 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 à-trous wavelet transform (AWT) and EMD in terms of quantitative analyses by Root Mean Squared Error (RMSE) and Mutual Information (MI).
Keywords: Multifocus Image Fusion; Empirical Mode Decomposition; ‘À-trous’ Wavelet Transform; Support Vector Machines Multifocus Image Fusion; Empirical Mode Decomposition; ‘À-trous’ Wavelet Transform; Support Vector Machines
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Chen, S.; Su, H.; Zhang, R.; Tian, J.; Yang, L. Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion. Sensors 2008, 8, 2500-2508.

AMA Style

Chen S, Su H, Zhang R, Tian J, Yang L. Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion. Sensors. 2008; 8(4):2500-2508.

Chicago/Turabian Style

Chen, Shaohui; Su, Hongbo; Zhang, Renhua; Tian, Jing; Yang, Lihu. 2008. "Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion." Sensors 8, no. 4: 2500-2508.


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