Blind Image Separation using Sparse Representation

This paper focuses on the blind image separation using their sparse representation in an appropriate transform domain. A new separation method is proposed that proceeds in two steps: (i) an image pre-treatment step to transform the original sources into sparse images and to reduce the mixture matrix to an orthogonal transform (ii) and a separation step that exploits the transformed image sparsity via an lscr_p-norm based contrast function. A simple and efficient natural gradient technique is used for the optimization of the contrast function. The resulting algorithm is shown to outperform existing techniques in terms of separation quality and computational cost.