Adaptive Boundary Effect Processing For Empirical Mode Decomposition Using Template Matching

Abstract

This paper is contributed to the boundary effect problem of the empirical mode decomposition algorithm, which results in a serious distortion in the EMD sifting process. An adaptive method for processing boundary effect in the empirical mode decomposition sifting process is presented, which has exploited the local time- or spatial- scales and the waveform or texture characteristics near boundary of the signal or image to extend the signal or image so that additional subsignal or subimage are obtained. The extended section is taken as the most suited subsignal or subimage to the inner signal or image by template matching operation. The multiple components of the original signal or image are available by applying EMD algorithm to the extended signal or image and then leaving out the extended parts of the decomposed components. Simulation results have proved that the proposed template matching based decomposition method outperforms the neural network extending method, the mirror extrema extending method and the AR model extending method for 1D signals, and perform texture extraction effectively for 2D natural images such as defect-free and defect fabrics.