Similarity modulated block estimation for image interpolation

Modeling the nonstationarity of image signals is one of the challenging issues for image interpolation. In this paper, we propose a similarity probability modeling to faithfully characterize the nonstationarity of image signals, and present a novel image interpolation algorithm based on the proposed model. The missing pixels are estimated in groups by weighted block estimation. The weight of each pixel inside the block is defined as the similarity probability between itself and the centered to-be-interpolated pixel. It is demonstrated by the experimental results that the proposed method preserves the edge structures of the interpolated images better than the state-of-the-art interpolation methods. Annoying artifacts nearby the sharp edges are also greatly reduced.