Abstract: “In this paper a watermark embedding and recovery technique based on the compressed sensing theorem is proposed. Both host and watermark images are sparsified using DWT. In recovery process, a new method called Least Support Matching Pursuit (LS-OMP) is used to recover the watermark and the host images in clean conditions. LS-OMP algorithm adaptively chooses optimum L (Least Part of support), at each iteration. This new algorithm has some important characteristics: it has a low computational complex-ity comparing with ordinary OMP method Also, we develop an invisible image watermarking algorithm in the presence of compressive sampling using the LS-OMP. Simulation results show that LS-OMP outperforms many algorithms”

Keywords: Compressed sensing, Lest Support Orthogonal Matching Pursuit, Orthogonal matching pursuit, restricted isometry property, Watermark