Spatial interpolation is an important technique that is often used to recover an image from its downsampled version, or to simply perform image expansion. Many conventional linear techniques exist, however, these often perform rather poorly in a subjective manner. In this paper, image interpolation is performed using a binary-based Gibbs random field (GRF) model. Images are interpolated from their downsampled versions along with a number of texture parameters that are estimated within smaller image blocks. These iterative GRF methods are subsequently approximated by a non-iterative nonlinear filtering operation, thereby reducing the computational complexity of the interpolation process. Experimental results indicate that the statistical GRF approaches adapt to textured regions as well as the smooth areas within an image, and thus, can achieve better results than the conventional linear schemes.