Separation of superimposed images with unknown motions using sparsity priors
When people take photos through a transparent surface, it is ubiquitous that the images are superimposed with two source images, the transmitted layer and the reflected layer. In this paper, we utilize the sparsity priors over image color distribution and image gradients, and we realize the separation of such images by exploiting the diversity (relative motions of source images) in different snapshots. Our approach can estimate the motion even when layer image is quite faint, extract layer gradients when layer intensity is unchanged, and obtain a reliable separation by considering the gradients as limitations. The effectiveness of our approach is shown in experiments on both synthetic images and real world photos.