Blind signal separation and identification of mixtures of images
This work addresses the problem of separating mixed images for applications in image processing, but it appears incremental as it builds on existing statistical techniques.
The paper tackles blind signal separation for image mixtures by combining second-order and higher-order statistics in the wavelet domain, resulting in promising outcomes for identifying images from noisy mixtures.
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.