A Compact Image Magnification Method with Preservation of Preferential Components

Image magnification has been attracting a great deal of attention for long, and many approaches have been proposed to date. Nevertheless, bicubic interpolation is still the standard approach since it can be easily computed and does not require a priori knowledge nor a complicated model. In spite of such convenience, the images enlarged bicubically are blurry, in particular for large magnification factors. In this paper, we propose a new method, which is as compact as bicubic interpolation, while performing better than it. The key technique we used in this method is a sampling theorem that preserves preferential components in input images. We show that, by choosing the edge enhancement components as the preferential components, the proposed method performs much better than bicubic interpolation, with the same, or even less amount of computation.