  In this paper, a fresh procedure to handle image mixtures by means of blind
signal separation relying on a combination of second order and higher order
statistics techniques are introduced. The problem of blind signal separation is
reassigned to the wavelet domain. The key idea behind this method is that the
image mixture can be decomposed into the sum of uncorrelated and/or independent
sub-bands using wavelet transform. Initially, the observed image is
pre-whitened in the space domain. Afterwards, an initial separation matrix is
estimated from the second order statistics de-correlation model in the wavelet
domain. Later, this matrix will be used as an initial separation matrix for the
higher order statistics stage in order to find the best separation matrix. The
suggested algorithm was tested using natural images.Experiments have confirmed
that the use of the proposed process provides promising outcomes in identifying
an image from noisy mixtures of images.
