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Adaptive image super-resolution via controlled weighting coefficients of a maximum-a-posteriori estimator

Abstract. Enhancing the spatial resolution of images has always been a hotspot in digital imaging. An adaptive multiframe image super-resolution (SR) algorithm has been proposed, which suppresses noise while preserving edges simultaneously. Based upon the maximum-a-posteriori (MAP) concept, the objective function of the SR algorithm consists of a regularization term and a data error term. The proposed adaptive algorithm introduces a set of weighting coefficients, which control the contribution between the regularization term and data error term in each of the estimated high-resolution pixels. The employed coefficients are defined according to the information of neighbors of the estimated pixel. Our proposed method is robust to the Gaussian noise and its destructive effect on image quality. Visual evaluation and numerical results in both of the real and synthetic images show that the performance of the proposed method is better than the other methods.