Application Of BEMD Method In Image Processing

The Empirical Mode Decomposition (EMD), which has been recently introduced by Huang in 1998, is a new method of signal processing and has superior performance in non-stationary signal analysis. This method can be applied to decompose the non-stationary data into a series of data layers with different character-scale and the trend data. It is adaptive, and, therefore, suitable for processing nonlinear and non-stationary data.Because of the superior performance in one-dimensional signal processing, the EMD is extended to Bidimensional EMD (BEMD) and then applied to process bidimensional signal. On account of the complexity of bidimensional signal, the general BEMD has some shortages and need to be improved in future.In this thesis, the theories of the empirical mode decomposition (EMD) and its extension Bidimensional EMD are introduced first. Then we describe the processing flow of the BEMD. The sifting process is realized using mathematical morphology operators to detect regional extrema and thanks to delaunay triangulation and Bernstein-Bezier spline interpolation based on triangular meshes for surface interpolation. In addition, according to the definition of the intrinsic mode function, we present a new condition to end the sifting process, and it is proved that the speed of decomposition increased.By the BEMD method, an image with noise is decomposed into a series of detail data and the trend data. The noise can be removed by the image reconstruction with the trend data. Experimental results demonstrate that comparing with mean filter, median filter, wiener filter, this method has more notable performance to get rid of the multiplicative noise and the value of PSNR markedly increased.