3D image interpolation based on directional coherence

Abstract

Image interpolation is of great importance in biomedical visualization and analysis. In this paper, we present a novel gray-level interpolation method called Directional Coherence Interpolation (DCI). The principle advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. Since the edges of the image and the contents of the objects are well preserved along the MCDs, DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary/surface. A number of experiments were performed on both synthetic and real medical images to evaluate the proposed approach. The experimental results show that in addition to the substantial improvement of visual effects (qualitative evaluation), the quantitative error measures of DCI are also better than the conventional gray level linear interpolation. Comparing with the shape-based interpolation scheme applied on gray-level images, DCI has much lower computation cost.