Separation of semireflective layers using sparse ICA

We address the problem of blind source separation (BSS) of superimposed images and, in particular, consider the recovery of a scene recorded through a semireflective medium (e.g. glass windshield) from its mixture with a virtual reflected image. We extend the sparse ICA (SPICA) approach to BSS and apply it to the separation of the desired image from the superimposed images, without having any a priori knowledge about its structure and/or statistics. Advances in the SPICA approach are discussed. Simulations and experimental results illustrate the efficiency of the proposed approach, and of its specific implementation in a simple algorithm of a low computational cost. The approach and the algorithm are generic in that they can be adapted and applied to a wide range of BSS problems involving one-dimensional signals or images.