Weighted Encoding Based Image Interpolation With Nonlocal Linear Regression Model

An image interpolation model based on sparse representation is proposed. Two widely used priors including sparsity and nonlocal self-similarity are used as the regularization terms to boost the performance of the interpolation model. Meanwhile, we incorporate nonlocal linear regression into this model, since nonlocal similar patches could provide a better approximation to a given patch. Moreover, we propose a new approach to learn an adaptive sub-dictionary online instead of clustering. For each patch, similar patches are grouped to learn the adaptive sub-dictionary, generating a more sparse and accurate representation. Finally, weighted encoding is introduced to suppress tailing of fitting residuals in data fidelity. Abundant experimental results show that our proposed method achieves better performance compared to several state-of-the-art methods in terms of subjective and objective evaluations.