A Study On Signal Extraction Based On Independent Component Analysis And Wavelet Transform

In the influence of apparatus and environment, there are some noise in every kind of images,so it is difficult to detect edges. There have been some algorithms on edge detection,but they can't get ideal results as noise in them.Wavelet transform provides special multiscales decomposition technology in time frequency domain,making signals and noise show different property and making adjacent scales' correlation.This paper present an improved edge detection algorithms-edge detection in noisy image based on wavelet interscales back shifting correlation : it resolves image by wavelet backing shiftingly multiply between adjacent wavelet coefficients to enhance edges , then form filtered image and filter some subimages to denoise. Simulation result shows this improved algorithm can better restrain noise than traditional edge detection algorithms.As the development of information technology ,human beings receive data containing information by sensors ,which are mixed by unknown sources. So it is necessary to separate mixed blind sources. Many independent component analysis(ICA) algorithms need iteration and large couting,which may be get divergence result. They are valid when sources are supergaussian or subgaussian,but can't be practical when mixed souces are supergaussian signal andsubgaussian signal toghter.After wavelet decomposition of signal, it gets approximated coefficients and detailed coefficients which are joined in the ICA optimization process.Then it builts a signal to noise ratio objective function based on peculiarity that better blind source separation result, higher signal to noise ratio.Most imformation of signal is shown by wavelet approximated coefficients,so this paper present ICA method with global optimal property based on wavelet transform: after building objective function ,let approximated coefficients as estimation of source signals, optimization is changed into eigenvalue decomposition.This method haven't any iteration,low complexity and counting from simulation result. Wavelet detailed coefficients are important part of signal,so it presents an improved algorithm: blind separation in supergaussian and subgaussian signals based on wavelet smoothing. Simulation result shows this improved method have same merit of preceding method, but better resemblant coefficients and separated effect.