Resolving permutation ambiguity in correlation-based blind image separation

We address the problem of permutation ambiguity in blind separation of multiple mixtures of multiple images (resulting, for instance, from multiple reflections through a thick grass plate or through two overlapping glass plates) with unknown mixing coefficients. In this paper, first we devise a generalized multiple correlation measure between one gray image and a set of multiple gray images and derive a decorrelation-based blind image separation algorithm. However, many blind image separation methods, including this algorithm, suffer from a permutation ambiguity problem that the success of the separation depends upon the selection of permutations corresponding to the orders of the update operations. To solve the problem, we improve the first algorithm above by decorrelating the mixtures while searching for the appropriate update permutation using a pruning technique. We show its effectiveness through experiments with artificially mixed images and real images.

► We address the problem of blind image separation. ► We introduce a multiple correlation measure among images and a pruning scheme. ► Separation of image mixtures is accomplished without permutation ambiguity. ► We show its effectiveness through experiments with artificial and real images.