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    Computes  correlation of two images or data cubes.  The result is an im-
    age (data cube) containing

          Out(i,j) = < In1(k-i,l-j)*In2(k,l) >         averaged over k,l
          For mode correlation (MODE$ = YES)
          Out(i,j) = < In1(k-i,l-j)**2 + In2(k,l)**2
                       - 2 * In1(k-i,l-j)*In2(k,l) >   averaged over k,l
          For mode square (MODE$ = NO)

    Actually, linear conversion formulas are used to  keep  the  correlation
    image meaningful in user coordinates.  The input images must match.

    When used for example to  recenter  images,  the  position  of  the max-
    imum  of the correlation image (or equivalently of the  minimum  of  the
    sum of squares  image)  yields  the  required  recentering.   MODE$  YES
    (Correlation)  is  to  be  used when the input distribution has a finite
    extent, while MODE$ NO (Square) can be used in any case, but is somewhat
    slower of course.

Gildas manager 2020-12-02