Edge-Directed Interpolation in a Bayesian Framework

In this paper we present a novel framework for Edge-Directed Interpolation (EDI) of still images. The problem is treated as finding maximum a posteriori estimates of each interpolated pixel type and intensity value. The pixel type may be one of the pre-defined edge directions or "non-edge". Instead of the separate steps of edge orientation detection and intensity interpolation, maximizing the joint probability density function of type and intensity provides a better fit to the local image structure. Such a technique allows an effective discrimination between edges and non-edges (uniform areas and texture), thus leading to the suppression of artifacts which are common to existing EDI methods. Objective and subjective comparisons with conventional EDI methods corroborate the advantages of the proposed one. Moreover, the locality and the low computational complexity of the method make it suitable for a hardware implementation.