Depth image super-resolution based on joint sparse coding

Abstract This paper proposes a new approach to single depth image super-resolution (SR), based upon a novel joint sparse coding model. A low-resolution color is used as a guide in the SR process. Firstly, we introduce synthetic characteristic image patch to learn a joint dictionary from the low-resolution depth map as well as its corresponding low-resolution intensity image. Then, we derive the joint nonlocal center sparse representation model based on sparse coding and theoretical analysis. In reconstruction process, we use Bayesian interpretation approach to estimation the sparse code coefficients for each unknown HR image patch. Meanwhile, we use an iterative algorithm to solve the JSC model. In addition, we exploit image patch redundancy within and across different scales, produce visually pleasing results without extensive training on external database. Experimental results demonstrate that the proposed method outperforms favorably many current state-of-the-art depth map super-resolution approaches on both visual effects and objective image quality and underpin the validity of our proposed model.