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 detec- tion 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 meth- ods. 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.