Adaptive autoregressive model with window extension via explicit geometry for image interpolation

In this paper, we propose a novel adaptive autoregressive (AR) model constructed with an explicit geometry based extended window for image interpolation. Geometric features are chosen as criterions to include more useful pixels. These features are estimated explicitly and guide the interpolation window to extend adaptively. To characterize the piecewise stationary of images, the patch-geodesic distance based similarity is proposed and modulated into the adaptive AR model. For increasing the precision of the parameter estimation, a weighted ridge regression based estimation is employed. With the estimation, the multicollinearity between parameters, which occurs in piecewise stationarity conditions, is eliminated. Experimental results demonstrate that the proposed method is better than or competitive with state-of-the-art interpolation methods in both objective and subjective quality evaluations.