Blind signal separation and identification of mixtures of images

TL;DR

This paper introduces a novel blind signal separation method for image mixtures using wavelet transforms combined with second and higher order statistics, improving the identification of images from noisy mixtures.

Contribution

It proposes a new approach that reassigns blind signal separation to the wavelet domain, integrating second and higher order statistics for better image separation.

Findings

Effective separation of noisy image mixtures demonstrated

Wavelet domain approach improves identification accuracy

Method outperforms traditional techniques in experiments

Abstract

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.