Image Blind Separation Algorithm Based on New ICA
LI Cong-xin · Jisuanji gongcheng · 2006
This paper presents a new image blind separation algorithm by using nonparametric entropy estimation.The new algorithm directly estimates m-spacing entropy according to mixture image signal X and avoids explicit probability density estimation.By exhaustive search of all possible rotation matrices,contrast function minimum and corresponding optimum rotation matrix is easily found without the presence of local minima trouble.It is fit for the diversity probability density distribution of image.Experiment tests show that it is a robust image separation algorithm with better performance than traditional ones such as FastICA,natural gradient(NG) and joint approximate diagonalization of eigen matrices(JADE) algorithm.