Analysis and characterization of super-resolution reconstruction methods

Reconstruction techniques exploit a first building process using Low-resolution (LR) images to obtain a "draft" High Resolution (HR) image and then update the estimated HR by back-projection error reduction. This paper presents different HR draft image construction techniques and shows methods providing the best solution in terms of final perceived/measured quality. The following algorithms have been analysed: a proprietary Resolution Enhancement method (RE-ST); a Locally Adaptive Zooming Algorithm (LAZA); a Smart Interpolation by Anisotropic Diffusion (SIAD); a Directional Adaptive Edge-Interpolation (DAEI); a classical Bicubic interpolation and a Nearest Neighbour algorithm. The resulting HR images are obtained by merging the zoomed LR-pictures using two different strategies: average or median. To improve the corresponding HR images two adaptive error reduction techniques are applied in the last step: auto-iterative and uncertainty-reduction.