Robust maximum a posteriori image super-resolution

Michalis Vrigkas, Christophoros Nikou, Lisimachos P. Kondi

Abstract: A global robust M-estimation scheme for maximum a posteriori (MAP) image super-resolution which efficiently addresses the presence of outliers in the low-resolution images is proposed. In iterative MAP image super-resolution, the objective function to be minimized involves the highly resolved image, a parameter controlling the step size of the iterative algorithm, and a parameter weighing the data fidelity term with respect to the smoothness term. Apart from the robust estimation of the high-resolution image, the contribution of the proposed method is twofold: (1) the robust computation of the regularization parameters controlling the relative strength of the prior with respect to the data fidelity term and (2) the robust estimation of the optimal step size in the update of the high-resolution image. Experimental results demonstrate that integrating these estimations into a robust framework leads to significant improvement in the accuracy of the high-resolution image.