This module produces a prolate convolution kernel for optimal
image reduction~\cite{landau.pollak:prolate}. 
It is similar to the \verb+prolatef+ module, but here
the support constraint is enforced in
the space domain.
Among all $n\times n$ images $u$ such that $u(x)=0$ 
outside a $s\times s$ window, the energy 
$$E(u) = \frac{\displaystyle \sum_{|\xi|\leq dn/2} |\hat u(\xi)|^2}
{\displaystyle \sum_{\xi} |\hat u(\xi)|^2}$$
is maximized ($\hat u$ is 
the Fourier Transform of $u$), yielding an optimal image $\bar u$.
The result is then normalized to produce a unit mass kernel 
$out$.

%\medskip

%The relative choice of the $d$ and $s$ parameters can be made easier
%by using the \verb+prolates+ module.

\medskip

Example of use (reduction of an image by a factor 2):

\begin{verbatim}
prolate -n 256 3 0.5 kernel
fview -z 100 kernel &
fconvol fimage kernel out
fsample out out 2
fview out &
\end{verbatim}
