Abstract
In this paper, we propose a robust image super-resolution (SR) algorithm that aims to maximize the overall visual quality of SR results. We consider a good SR algorithm to be fidelity preserving, image detail enhancing and smooth. Accordingly, we define perception-based measures for these visual qualities. Based on these quality measures, we formulate image SR as an optimization problem aiming to maximize the overall quality. Since the quality measures are quadratic, the optimization can be solved efficiently. Experiments on a large image set and subjective user study demonstrate the effectiveness of the perception-based quality measures and the robustness and efficiency of the presented method.
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