Adaptive general scale interpolation based on similar pixels weighting

Abstract

In this paper, we propose an adaptive general scale interpolation algorithm considering the non-stationarity of natural images in local areas. In image 2× enlargement, there are fixed relative positions between low-resolution (LR) pixels and high-resolution (HR) pixels. Unknown HR pixels can be estimated by their available LR neighbors. However, such relative positions are not fixed in the general-scale enlargement situations. The number and position of available LR pixels are indeterminate, therefore HR pixels can not be estimated by LR pixels. To make our method suitable for general scaling factors, we construct autoregressive (AR) models with pixels' neighbors instead of their available LR neighbors. Simultaneously, we introduce the similarity between pixels within a local window, which improves the method's performance by modeling the non-stationarity of image signals. Experimental results demonstrate the effectiveness of the proposed method on general scaling factors.