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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