Bidimensional Empirical Mode Decomposition And Its Application In Image Processing

Empirical Mode Decomposition (EMD) is a new signal processing method, which has been recently introduced by Huang in 1998. It is fundamentally different from the traditional Fourier transform and Wavelet transform, realize the signal multi-scale decomposing. This method is adaptive and suitable for processing non-linear and non-stationary date. Bidimensional empirical mode decomposition(BEMD) is the extension and expansion of one-dimensional EMD, which has been widely used in processing bidimensional signal. On account of the complexity in bidimensional signal, the general BEMD method has some defects which need to be resolved.In this thesis, the decomposition process and some key characteristics of one-dimensional EMD is discussed in details, describe the Time-spectrum analysis theory of one-dimensional EMD, and give a specific application of EMD in one-dimension signal.This thesis is mainly introduced the principle and the decompose steps of BEMD, and discuss the implement of several key algorithms in BEMD framework. Analyze and compare the two detect regional extrema methods, the 8-neighborhood comparison method and the mathematical morphology. The upper and lower envelopes of the original image are based on two methods, which are delaunay triangulation and radial basis function interpolation, the boundary effect is solved by mirror symmetry. put forward a new condition to end the sifting process, propose two thresholds based on the envelope mean and the extreme point difference to restrain IMF decomposing process. At last, using two BEMD methods to decompose Lena image.Using the improved BEMD algorithm to process image. A noise removing method based on IMF threshold function is proposed, it avoids the shortcomings of blurred edges in using traditional BEMD method, Experiments show that the method has better results. At last, the BEMD is applied in image edge detection. Detecting the edge feature form all of the IMF, then output the final summary of image edge feature set, Experiments verify the validity of the method.