Single Depth Image Super-resolution Research Articles

Super resolution of single depth image based on multi-dictionary learning with edge feature regularization

Currently, the acquisition and application of depth images are attracting a lot of attention thanks to the rapid development of 3D video. However, the current depth cameras cannot obtain high quality depth images due to the limitations in the imaging system. In this paper, to address this issue, we propose a scheme for single depth image super resolution based on multi-dictionary learning with edge regularization model. In the training stage, we focus on the edge information that represents the structure of the image, and extract the edge part, the low frequency and high frequency parts from the high resolution depth image set respectively. After that, three dictionaries are learned for the three parts with the constraint of the same sparse representation. In the image synthesis stage, we employ an edge-preserving regularization model as a reconstruction constraint to preserve the sharp structure, and reconstruct the depth image via the dictionaries learned from the training stage. Experimental results show that our proposed method can achieve good results in edge preservation, and both PSNR and SSIM values of the reconstructed depth images are superior to the state-of-art methods.

Depth image super-resolution based on joint sparse coding

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.

Edge-Guided Single Depth Image Super Resolution.

Recently, consumer depth cameras have gained significant popularity due to their affordable cost. However, the limited resolution and the quality of the depth map generated by these cameras are still problematic for several applications. In this paper, a novel framework for the single depth image superresolution is proposed. In our framework, the upscaling of a single depth image is guided by a high-resolution edge map, which is constructed from the edges of the low-resolution depth image through a Markov random field optimization in a patch synthesis based manner. We also explore the self-similarity of patches during the edge construction stage, when limited training data are available. With the guidance of the high-resolution edge map, we propose upsampling the high-resolution depth image through a modified joint bilateral filter. The edge-based guidance not only helps avoiding artifacts introduced by direct texture prediction, but also reduces jagged artifacts and preserves the sharp edges. Experimental results demonstrate the effectiveness of our method both qualitatively and quantitatively compared with the state-of-the-art methods.