Reconstruction of hidden images using wavelet transform and an entropy-maximization algorithm

This paper proposes a blind image separation method using wavelet transform and an entropy-maximization algorithm. Our blind separation algorithm is an improved version of the entropy-maximization algorithms presented by Bell-Sejnowsky and Amari. These algorithms work well for signals having a superGaussian distribution, such as speech and audio. The proposed method is to apply the improved algorithm to the wavelet coefficients of a natural image, whose distribution is close to superGaussian. Our method successfully reconstruct twelve images hidden in another twelve images which are similar each other.