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Image interpolation is a special case of image super-resolution, where thelow-resolution image is directly down-sampled from its high-resolutioncounterpart without blurring and noise. Therefore, assumptions adopted insuper-resolution models are not valid for image interpolation. To address thisproblem, we propose a novel image interpolation model based on sparserepresentation. Two widely used priors including sparsity and nonlocalself-similarity are used as the regularization terms to enhance the stabilityof interpolation model. Meanwhile, we incorporate the nonlocal linearregression into this model since nonlocal similar patches could provide abetter approximation to a given patch. Moreover, we propose a new approach tolearn adaptive sub-dictionary online instead of clustering. For each patch,similar patches are grouped to learn adaptive sub-dictionary, generating a moresparse and accurate representation. Finally, the weighted encoding isintroduced to suppress tailing of fitting residuals in data fidelity. Abundantexperimental results demonstrate that our proposed method outperforms severalstate-of-the-art methods in terms of quantitative measures and visual quality.

Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model