Regularization-based reconstruction is an important single-image super-resolution (SR) method. This class of methods aims to design effective image priors and incorporate them into a regularization framework to enhance edge-and texture-preserving capabilities during the SR process. In this study

a global and local structural content adaptive regularization model is proposed to solve the single-image SR problem. This model combines the global non-Gaussian statistics of an image gradient with the orientation-adaptive regression property of a local structure. Generalized Gaussian distribution is applied to fit the heavy-tailed distribution of the image gradient. A global content-based sparsity measure (0< <1) norm is constructed under the maximum a posterior probability framework. The anisotropic correlation of local content is employed to construct an adaptive regression prior of the local structure based on the Geman-McClure function. Finally

a half-quadratic penalty method and a variable splitting technique are used to solve the model effectively.For an objective assessment

experimental results demonstrate that the quality of the SR images obtained by the proposed method is better than those obtained by other methods in terms of peak signal-to-noise ratio and structural similarity. For a subjective evaluation

the proposed method can retain edge and image details effectively. The proposed adaptive regularization method can preserve edge and image details effectively in single-image super resolution.