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.

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