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Bidimensional empirical mode decomposition (BEMD) algorithm, with high adaptive ability, provides a suitable tool for the noisy image processing, and, however, the edge effect involved in its operation gives rise to a problem—how to obtain reliable decomposition results to effectively remove noises from the image. Accordingly, we propose an approach to deal with the edge effect caused by BEMD in the decomposition of an image signal and then to enhance its denoising performance. This approach includes two steps, in which the first one is an extrapolation operation through the regression model constructed by the support vector machine (SVM) method with high generalization ability, based on the information of the original signal, and the second is an expansion by the closed-end mirror expansion technique with respect to the extrema nearest to and beyond the edge of the data resulting from the first operation. Applications to remove the Gaussian white noise, salt and pepper noise, and random noise from the noisy images show that the edge effect of the BEMD can be improved effectively by the proposed approach to meet requirement of the reliable decomposition results. They also illustrate a good denoising effect of the BEMD by improving the edge effect on the basis of the proposed approach. Additionally, the denoised image preserves information details sufficiently and also enlarges the peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Abstract: The treatment of end effects is one of the most important open problems related to the EMD (Empirical Mode Decomposition) method. This work proposes a new approach that couples the mirror expansion with the extrapolation prediction of regression function to solve this problem. The algorithm includes two steps: the extrapolation of the signal through Support Vector (SV) regression at both endpoints to form the primary expansion signal, then the primary signal is further expanded through extrema mirror expansion and EMD is performed on the resulting signal to obtain reduced end effects. If there is not enough length for the signal to meet the need of finding the length of the data available for expanding the signal, a direct extrapolation towards the outside of the signal at the endpoint is executed by the estimate model, and the length of extrapolation points is controlled by the first local extremum. Applications of the proposed approach to the decomposition of a digital modeling signal and three segment signals from the observed earthquake signal by the EMD method are presented, and all of the results are compared with those on the basis of the traditional mirror expansion approach and the extrapolation estimate expansion based on the SV regression, which shows that the most satisfactory result can be obtained for the elimination of end effects in EMD method by mirror image coupled with SV regression. [Copyright &y& Elsevier]

End effects of EMD (empirical mode decomposition) make a serious distortion of the decomposition result. In order to reduce the end effects in the process of decomposition, support vector machine (SVM) which is a kind of intelligent algorithm is combined with EMD, then a solution to the end effects problem during the course of decomposition using SVM model is proposed. Firstly, one or more extreme values are obtained by extending two endpoints of the original data with SVM. Moreover, in order to get more reasonable extension at endpoint, SVM algorithm is combined with particle swarm algorithm (PSO) to optimize the parameters, and the extension of two endpoints will be more accurate, then the end-points of cubic spline curve will not have large swing so as to achieve that intrinsic mode functions (IMF) of EMD are more accurate and reliable. Simulation results indicate that the extension method for data based on PSO-SVM method can restrain the end effects effectively. [ABSTRACT FROM AUTHOR]

A technique to predict the gas emission from the coalface is presented which is realized by the intrinsic mode SVM modeling on the basis of the intrinsic mode components drawn out from the observed dada by means of the EMD (empirical mode decomposition) method. The intrinsic mode functions, that is, IMF components, are obtained by the EMD analysis of the historical recording dada of gas emission, and after the prediction of each intrinsic mode is carried out by the extrapolation of its regression function determined by the SVM flmction regression approach, then the prediction result of gas emission is derived through the reconstruction summing all prediction results corresponding to different intrinsic modes. From an application example related to the monitoring data it can be seen that the introduction of EMD method into ther theoretical modeling to predict the gas emission from the coalface obviously improves the accuracy in comparison with the conventional SVM method, to have the prediction results agreement with the monitoring data. The theoretical analysis shows that in modeling the gas emission from the coalface, the extraction of intrinsic modes and the operation of SVM method make full use of the sampling data driven adaptive performances, and hence provide better theoretical flmdamentals for guarding perfect prediction efficiency. [ABSTRACT FROM AUTHOR]