  Undersampled images, such as those produced by the HST WFPC-2, misrepresent
fine-scale structure intrinsic to the astronomical sources being imaged.
Analyzing such images is difficult on scales close to their resolution limits
and may produce erroneous results. A set of ``dithered'' images of an
astronomical source generally contains more information about its structure
than any single undersampled image, however, and may permit reconstruction of a
``superimage'' with Nyquist sampling. I present a tutorial on a method of image
reconstruction that builds a superimage from a complex linear combination of
the Fourier transforms of a set of undersampled dithered images. This method
works by algebraically eliminating the high order satellites in the periodic
transforms of the aliased images. The reconstructed image is an exact
representation of the data-set with no loss of resolution at the Nyquist scale.
The algorithm is directly derived from the theoretical properties of aliased
images and involves no arbitrary parameters, requiring only that the dithers
are purely translational and constant in pixel-space over the domain of the
object of interest. I show examples of its application to WFC and PC images. I
argue for its use when the best recovery of point sources or morphological
information at the HST diffraction limit is of interest.
