This module produces a prolate convolution kernel for optimal
image reduction~\cite{landau.pollak:prolate}. 
It is similar to the \verb+prolate+ module, but here
the support constraint is enforced in
the Fourier domain, so that a bandlimited kernel is obtained 
(which allows aliasing-free image reduction).
Among all $n\times n$ images $u$ such that 
$\hat u(\xi)=0$ for any $\xi$ outside a disc or square 
domain $F$ in Fourier space, the energy 
$$E(u) = \frac{\displaystyle \sum_{\-s/2\leq x,y \leq s/2} u(x,y)^2}
{\displaystyle \sum_{x,y} u(x,y)^2}$$
is maximized. The result is then normalized to produce a (bandlimited)
unit mass kernel whose Fourier Transform is $out$.
The domain $F$ is specified with the options \verb+-d+ (size) and 
\verb+-c+ (shape). The constraint $\hat u \geq 0$ can be enforced 
with the \verb+-p+ option.

\medskip

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

\begin{verbatim}
prolatef -n 256 3 0.5 kernel
fview kernel &
fftconvol fimage kernel out
fsample out out 2
fview out &
\end{verbatim}
