Novel autoregressive model based on adaptive window-extension and patch-geodesic distance for image interpolation

In this paper, we propose a novel autoregressive (AR) model based on the adaptive window and the patch-geodesic distance for the image interpolation. The model combines the information of inner/inter-patch correlation. To model the inner-patch correlation, we introduce a patch-geodesic distance similarity metric. The proposed metric shows the desirable capacity to depict the piecewise-stationarity of natural images. For the inter-patch correlation, we introduce the inter-patch structure variation and propose an adaptive window-extension AR model. The model extends the interpolation window according to the local structural variation, increasing the adaptation without violating the consistency. Comprehensive experiments demonstrate that the proposed method is better than or competitive with state-of-the-art interpolation methods in both objective and subjective quality evaluations.