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

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

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China
* Author to whom correspondence should be addressed.
Received: 3 March 2008; Accepted: 31 March 2008 / Published: 8 April 2008
PDF Full-text Download PDF Full-Text [2386 KB, uploaded 16 September 2008 11:02 CEST]
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

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

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.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert