A new BSS algorithm based on the data fusion and ICA

Blind Signal Separation (BSS) and Independent Component Analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or ldquosourcesrdquo from observed mixtures. When the number of independent components is not very many, using ICA algorithm to separate mixed images, the separated images will have a very strong image artifact noise which will affect the separation efficiency of ICA. In this paper, data fusion technology will be carried out with ICA, firstly, the algorithm used Weiner Filter to preprocess data, and then center the data to make its mean zero; whiten data to make it decorrelation; secondly, use a fast fixed-point algorithm based on negentropy to separate the data, finally, use data fusion based on wavelet algorithm. The results showed that the images after treated by this algorithm become clearer, easier identification, noise significant inhibitory effect. Comparing with the images Peak Signal Noise Ratio (PSNR) before and after the fusion, it proves that the algorithm is feasibility and effectiveness.