Direction Adaptive Super-Resolution Imaging

In this paper a novel edge-preserving super-resolution (SR) image reconstruction method is proposed. The proposed maximum a-posteriori (MAP) based estimator uses gradient direction and amount for optimal noise reduction while preserving the edges. Compared to the other edge-preserving methods, the proposed algorithm uses the gradient direction for optimum regularization. The proposed method estimates gradient amplitude and direction at each iteration. This gradient map guides the SR reconstruction stage through iterations. Proposed method is compared against other traditional super resolution methods. Peak-signal-to-noise-ratio (PSNR) measures and illustrations clearly show that the proposed method is successful especially on edge structures in images.