Post-sampling aliasing control for natural images
Dinei A. F. Florencio, Ronald W. Schafer · 2002
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.