Autoregressive image interpolation via context modeling and multiplanar constraint

In this paper, we propose a novel image interpolation algorithm by context-aware autoregressive (AR) model and multiplanar constraint. Different from existing AR based methods which employ predetermined reference configuration to predict pixel values, the proposed method considers the anisotropic pixel dependencies in natural images and adaptively chooses the optimal prediction context by utilizing the nonlocal redundancy to interpolate pixels. Furthermore, the multiplanar constraint is applied to enhance the correlations within the estimation window by exploiting the self-similarity property of natural images. Similar patches are collected by the combination of patch-wise pixel values and the gradient information. And the inter-patch dependencies are adopted to improve the interpolation. The experimental results show that our method is effective in image interpolation and successfully decreases the artifacts nearby the sharp edges. The comparison experiments demonstrate that the proposed method can obtain better performance than other related ones in terms of both objective and subjective results.