A maximal interest-point strategy applied to image enhancement with external priors

We examine the problem of matching patches of internal and external data. A new patch quality indicator is proposed which aims at maximizing the ratio of the number of points of interest to the area. We prove that the best convex polygon maximizing the indicator is the triangle. Thus data-driven triangle patches are used in the patch-matching algorithm, considerably improving the matching quality. The triangle representation inherently allows affine correction of the patches, thereby increasing the matching probability and enabling a smaller external data-set. We employ this technique for image quality enhancement of low resolution facial data using a data bank of external faces.