General Scale Interpolation Via Context-Aware Autoregressive Model And Multiplanar Constraint

In this paper, we propose a novel image interpolation algorithm suitable for general scale enlargement. Different from previous ARbased interpolation algorithms which employ predetermined reference configuration to predict pixel values, we consider the context information when building AR models. Optimal references are selected by incorporating nonlocal-based correlation coefficient and the indicator for local edge direction. Furthermore, the multiplanar constraint among similar patches is applied to enhance the correlation within the estimation window and serves as a kind of supplement to data fidelity term in AR model. The experimental results show that our method is effective in several enlargement scales and successfully alleviate the artifacts nearby edges and preserve their sharpness. The comparison experiments demonstrate that the proposed method can obtain desirable performance in terms of both objective and subjective results.