Image interpolation systems are used to render a high resolution version of an image from a lower resolution representation. Conventional interpolation systems such as bilinear interpolation and nearest neighbor interpolation often perform poorly (in a subjective sense) when acting on a spatial region of an image which has an oriented structure such as an edge, line, or corner. Recently, systems based on directional interpolation have been presented which yield improved performance on these oriented structures. However, separate models are used for the detection of edges, lines, and corners. In this work, we combine simple building blocks (a Sobel edge detector, directional interpolation, and the directional filter bank) to form a system which exploits the orientational tuning and the spatial-frequency variant sensitivity of the human visual system. The new system handles all of the oriented features in the same manner. First, the image to be processed is split into its directional components. These directional components are then individually interpolated using the directional interpolation system. Since orthogonal (or nearly orthogonal components) are contained in different directional components, corners do not exist in the directional components. Furthermore, since the directional filter bank is an exactly reconstructing structure, the representation of the image in terms of its directional components is complete and thereby an invertible decomposition. In the absence of an oriented component, the directional interpolation system reverts to bilinear interpolation (though any other interpolant could be used).