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Test results for leveled inverse interpolation

Figures 26 and 27 show the same example as in Figures [*] and [*]. What is new here is that the proper PEF is not given but is determined from the data. Figure 26 was made with a three-coefficient filter $ (1,a_1,a_2)$ and Figure 27 was made with a five-coefficient filter $ (1,a_1,a_2,a_3,a_4)$ . The main difference in the figures is where the data is sparse. The data points in Figures [*], 26 and 27 are samples from a sinusoid.

subsine3
Figure 26.
Interpolating with a three-term filter. The interpolated signal is fairly monofrequency.
subsine3
[pdf] [png] [scons]

subsine5
Figure 27.
Interpolating with a five term filter.
subsine5
[pdf] [png] [scons]

Comparing Figures [*] and [*] to Figures 26 and 27 we conclude that by finding and imposing the prediction-error filter while finding the model space, we have interpolated beyond aliasing in data space.

Sometimes PEFs enable us to interpolate beyond aliasing.


next up previous [pdf]

Next: Analysis for leveled inverse Up: LEVELED INVERSE INTERPOLATION Previous: LEVELED INVERSE INTERPOLATION

2013-07-26