We present an efficient deinterlacing method via mathematical modeling of the neighbor pixels in the local region. The local surface model is designed using the quadratic equation having two-dimensional coordinate variables. Unlike conventional deinterlacing methods, the proposed method avoids using directional difference measures, resulting in reduced limitation on the number of considering edge directions. By modeling the local surface, it is easier to derive the true characteristic of the local region in a natural image than utilizing the directional difference measure. In order to decide the optimal coefficients of the surface model, the neighbor pixels around the current pixel to be interpolated are utilized. Once the coefficients are determined, the surface model estimates the pixel intensity of the current pixel to be interpolated. Simulation results show that the proposed surface model-based deinterlacing method minimizes the interpolation error. Compared to the traditional deinterlacing methods and Wiener filter-based interpolation method, the proposed method improves the subjective quality of the interpolated edges while maintaining a higher peak signal-to-noise-ratio level.