Blind separation of locally smooth images based on genetic algorithm
In this paper, a novel method based on genetic algorithm (GA) is proposed to separate blind image whose key point is the formulation of the new matrix which is a generalized permutation of the original mixing matrix. Since entropy minimization is closely associated with the smooth degree of source images, blind image separation is formulated to an entropy minimization problem by using the property that most of near pixels are smooth. A new dataset can be obtained by multiplying the mixed matrix by the inverse of the new matrix. Thus, the GA technique is used to searching for the lowest entropy values of the new data. Accordingly, the separation weight vector associated with the lowest entropy values can be obtained. Compared with the conventional independent component analysis (ICA) algorithm, the original signals in the proposed algorithm are not required to be independent. Simulation results on mixed images are employed to further illustrate the advantages of the proposed method.