Structural similarity-based nonlocal edge-directed image interpolation
Image interpolation is important for computer vision. Most of the existing image interpolation methods are based on the optimization in the mean square error (MSE) sense. In this paper, we incorporate the structural similarity (SSIM) based metric into the framework of the nonlocal edge-directed image interpolation (NLEDI) method. In the proposed algorithm, a missing pixel is interpolated using the weighted average of neighboring patches where the weights are determined by the SSIM-based metric instead of the MSE measurement. Simulations show that our proposed structural similarity-based NLEDI (SSNLEDI) scheme outperforms existing image interpolation methods and has higher PSNR values and better visual qualities.