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Post-Sampling Aliasing Control for Natural Images

Dinei A. Florencio and Ronald W. Schafer


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. In this paper we analyze the possibility of using non-linear filtering in the interpolation of subsampled images. We show that a very simple (5x5) non-linear reconstruction filter outperforms (for the images analyzed) linear filters of up to 256x256, including optimum (separable) Wiener filters of any size.


Publication typeInproceedings
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