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Super-resolution is a key task when browsing huge image databases over the Web; in fact, it allows for significant improvements of the service interactivity by increasing the image spatial resolution so that only thumbnail version of the images can be sent over the network. In the proposed work, the low-resolution image is first analyzed to identify several features that are significant for visual rendering and scene understanding. Such a classification is based on local frequency composition: uniform regions, edges and textures. The identified regions are then treated differently depending on the relative visual significance. Each region is further analyzed and a different interpolation approach is adopted, ranging from plain linear interpolation for homogeneous areas to edge area analysis and selective anisotropic interpolation. The combination of image region classification and adaptive-anisotropic interpolation is the main novelty of the proposed approach, which proved to outperform alternative techniques.