Image resampling is used for several purposes such as picture enlargement, image reconstruction, correcting for geometrical distortions and obtaining sub-pixel accuracy. Most of these uses are invaluable for medical, defense and other applications. Most of the resampling and interpolation methods documented in the literature could be grouped under one of two categories; conventional or adaptive. In conventional methods an interpolation function is applied indiscriminately to the whole image. No matter how complex the chosen function is, the resulting image in general suffers from aliasing, edge blurring and other artifacts. Adaptive methods, on the other hand aim at avoiding these problems by analyzing the local structure of the source image and using different interpolation functions and different areas of support. In this paper we present an adaptive algorithm for image resampling manly for zooming up. The algorithm is based on segmenting the image dynamically into homogeneous areas and preserving edge locations and their contrast. Based on the location of the pixel to be computed (within a homogenous area, on its edge or an isolated feature) interpolation, extrapolation or pixel replication is chosen. Algorithm performance (quality and computational complexity) is compared to different methods and functions previously reported in the literature and used and in most of the commercial packages. The advantage of the method is quite apparent at edges and for large zooming factors.