A New Image Scaling Algorithm Based on the Sampling Theorem of Papoulis and Application to Color Images

We present in this paper a new image scaling algorithm which is based on the generalized sampling theorem of Papoulis. The main idea consists in using the first and second derivatives of an image in the scaling process. The derivatives contain information about edges and discontinuities that should be preserved during resizing. The sampling theorem of Papoulis is used to combine this information. We compare our algorithm with nine of the most common scaling algorithms and two measures of quality are used: the standard deviation for evaluation of the blur, and the curvature for evaluation of the aliasing. The results presented here show that our algorithm gives the best images with very few aliasing, good contrast, good edge preserving and few blur. We also present how our algorithm applies to color images.