This study proposes an edge-preserving image interpolation algorithm for both clean images and polluted images. First, the structure of an image window is learnt adaptively by using a spatial general autoregressive (S...
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This study proposes an edge-preserving image interpolation algorithm for both clean images and polluted images. First, the structure of an image window is learnt adaptively by using a spatial general autoregressive (SGAR) model that is a uniform expression for both linear and non-linear autoregressive (AR) models. Parameters of the SGAR model are estimated in a moving window in the input low-resolution image by using the robust generalised M-estimator. Next, the interpolation model is established from the learnt model and a new feedback mechanism in accordance with the residual sum of squares minimisation principle. Finally, the gradient simulated annealing algorithm is used to solve the interpolation model, which can rapidly converge to the global optimum in probability with the help of gradient information. Experiments have been performed using worldwide datasets to evaluate the performance of the authors method. The results demonstrate that their method is superior to a recent AR model-based method and is bicubic, especially when images are polluted by noise such as Gaussian noise, Poisson noise, impulse noise, or a combination of these.
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