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{\plain Signal{ Restoration with the Parametric
 Wiener Filter}}
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{\plain {Ken Castleman, 3/24/94}}
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{\plain PWF_1D{.MCD}}
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{\plain {Section 16.2.4}}
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{\plain {The Gaussian:}}
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{\plain The signal, f(x), has been smoothed
 by H(u) and corrupted by additive white
 noise of power N.}
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{\plain The smoothing MTF:}
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{\plain {The power spectra:}}
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{\plain {The transfer function:}}
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{\plain {\f1 g} = 0.2}
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{\plain {\f1 g}{ = 0\par }{\f1 g}{ = 0.05\par 
}{\f1 g}{ = 0.2\par }{\f1 g}{ = 0.5\par 
}{\f1 g}{ = 1\par H(u)}}
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{\plain {Parametric Wiener Filter Transfer
 Function}}
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{\plain Note that {\f1 g} = 0 is straight
 deconvolution, {\f1 g} = 1 is Wiener deconvolution,
 and 0 < {\f1 g} < 1 is everything in between.\par 
In this case the signal dominates the noise
 over almost the entire spectrum, but the
 blurring is rather severe.\par {\f1 g} 
= 0.02 gives a pretty good looking restoration
 MTF. Wiener deconvolution is usually too
 mild.}
}