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<H2><A NAME="SECTION00511000000000000000">
Image sampling</A>
</H2>
The acquisition of a data frame involves a spatial sampling and digitalization
of the continuous image formed in the focus plane of a telescope.
The image may be recorded analog (e.g. on photographic plates) for later
measurements or acquired directly when digital detectors such as diode 
arrays and CCD's are used.
The individual pixel values are obtained by convolving the continuous
image <I>I</I>(<I>x</I>,<I>y</I>) with the pixel response function <I>R</I>(<I>x</I>,<I>y</I>).
With a sampling step of <IMG
 WIDTH="36" HEIGHT="22" ALIGN="BOTTOM" BORDER="0"
 SRC="img15.gif"
 ALT="$\Delta x$">
and <IMG
 WIDTH="36" HEIGHT="41" ALIGN="MIDDLE" BORDER="0"
 SRC="img16.gif"
 ALT="$\Delta y$">
the digital frame
is given by
<BR><P></P>
<DIV ALIGN="CENTER">

<!-- MATH: \begin{equation}
F_{i,j} = \int I(x,y) R(x-i\Delta x, y-j\Delta y) \; dx dy
+ N_{i,j}
\end{equation} -->

<TABLE WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE"><TD ALIGN="CENTER" NOWRAP><A NAME="eq:image">&#160;</A><IMG
 WIDTH="460" HEIGHT="54"
 SRC="img17.gif"
 ALT="\begin{displaymath}F_{i,j} = \int I(x,y) R(x-i\Delta x, y-j\Delta y) \; dx dy
+ N_{i,j}
\end{displaymath}"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
(2.1)</TD></TR>
</TABLE>
</DIV>
<BR CLEAR="ALL"><P></P>
where <I>N</I> is the acquisition noise.
This convolution is done analog in most detectors except for imaging
photon counting systems where it partly is performed digitally.
The sampling step and response function are determined normally by the 
physical properties of the detector and the acquisition setup.
The variation of the response function may be very sharp as for most 
semi-conductor detectors or more smooth as in image dissector tubes.
If the original image <I>I</I> is band width limited (i.e. only contains 
features with spatial frequencies less than a cutoff value <IMG
 WIDTH="28" HEIGHT="39" ALIGN="MIDDLE" BORDER="0"
 SRC="img18.gif"
 ALT="$\omega_c$">)
all information is retained in the digitized frame when the sampling
frequency 
<!-- MATH: $\omega_s = 2\pi/\Delta x$ -->
<IMG
 WIDTH="123" HEIGHT="44" ALIGN="MIDDLE" BORDER="0"
 SRC="img19.gif"
 ALT="$\omega_s = 2\pi/\Delta x$">
satisfies the Nyquist criterion:
<BR><P></P>
<DIV ALIGN="CENTER">

<!-- MATH: \begin{equation}
\omega_s = 2 \; \omega_c.
\end{equation} -->

<TABLE WIDTH="100%" ALIGN="CENTER">
<TR VALIGN="MIDDLE"><TD ALIGN="CENTER" NOWRAP><A NAME="eq:nyquist">&#160;</A><IMG
 WIDTH="95" HEIGHT="38"
 SRC="img20.gif"
 ALT="\begin{displaymath}\omega_s = 2 \; \omega_c.
\end{displaymath}"></TD>
<TD WIDTH=10 ALIGN="RIGHT">
(2.2)</TD></TR>
</TABLE>
</DIV>
<BR CLEAR="ALL"><P></P>
In Equation&nbsp;<A HREF="node12.html#eq:nyquist">2.2</A> it is assumed that <I>R</I> is a Dirac delta
function.
This means that only features which are larger than <IMG
 WIDTH="47" HEIGHT="22" ALIGN="BOTTOM" BORDER="0"
 SRC="img21.gif"
 ALT="$2\Delta x$">
can
be resolved.
A frame is oversampled when 
<!-- MATH: $\omega_s > 2\omega_c$ -->
<IMG
 WIDTH="90" HEIGHT="39" ALIGN="MIDDLE" BORDER="0"
 SRC="img22.gif"
 ALT="$\omega_s > 2\omega_c$">
while for 
smaller sample rates it is undersampled.

<P>
In astronomy the band width of an image is determined by the point spread 
function (PSF) and has often no sharp cutoff frequency.
Many modern detector systems are designed to have a sampling step only
a few times smaller than the typical full width half maximum (FWHM) of
seeing disk or PSF.
Therefore they will not fully satisfy Equation&nbsp;<A HREF="node12.html#eq:nyquist">2.2</A> and
tend to be undersampled especially in good seeing conditions.

<P>
A typical assumption in image processing algorithms is that the pixel
response function <I>R</I> can be approximated by a Dirac delta function.
This is reasonable when the image intensity does not vary significantly 
over <I>R</I> as for well oversampled frames where the effective size of <I>R</I>is roughly equal to the sample step.
If it is not the case, the effects on the algorithm used should be checked.
Interpolation of values between existing pixels is often necessary e.g. 
for rebinning.
Depending on the shape of <I>R</I> and band width of the image different schemes 
may be chosen to give the best reproduction of the original intensity
distribution.
In many cases low order polynomial functions are used (e.g. zero or first 
order) while sinc, spline or gaussian weighted interpolation may be more 
appropriate for some applications.

<P>
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<ADDRESS>
<I>Petra Nass</I>
<BR><I>1999-06-15</I>
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