Post-sampling aliasing control for natural images

Abstract

Sampling and reconstruction are usually analyzed under the framework of linear signal processing. Powerful tools like the Fourier transform and optimum linear filter design techniques, allow for a very precise analysis of the process. In particular, an optimum linear filter of any length can be derived under most situations. Many of these tools are not available for non-linear systems, and it is usually difficult to find an optimum non-linear system under any criteria. The authors analyze the possibility of using non-linear filtering in the interpolation of subsampled images. They show that a very simple (5/spl times/5) non-linear reconstruction filter outperforms (for the images analyzed) linear filters of up to 256/spl times/256, including optimum (separable) Wiener filters of any size.