%0 Journal Article
%T Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
%A Shaohui Chen
%A Hongbo Su
%A Renhua Zhang
%A Jing Tian
%J Sensors
%D 2008
%I MDPI AG
%X 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     -trous wavelet transform (AWT) and EMD in terms of quantitative analyses by Root Mean Squared Error (RMSE) and Mutual Information (MI).
%K Multifocus Image Fusion
%K Empirical Mode Decomposition
%K бщ ?     ?-trous бщ ?   Wavelet Transform
%K Support Vector Machines
%U http://www.mdpi.com/1424-8220/8/4/2500/