  A new technique is presented for producing images from interferometric data.
The method, ``smear fitting'', makes the constraints necessary for
interferometric imaging double as a model, with uncertainties, of the sky
brightness distribution. It does this by modelling the sky with a set of
functions and then convolving each component with its own elliptical gaussian
to account for the uncertainty in its shape and location that arises from
noise. This yields much sharper resolution than CLEAN for significantly
detected features, without sacrificing any sensitivity. Using appropriate
functional forms for the components provides both a scientifically interesting
model and imaging constraints that tend to be better than those used by
traditional deconvolution methods. This allows it to avoid the most serious
problems that limit the imaging quality of those methods. Comparisons of smear
fitting to CLEAN and maximum entropy are given, using both real and simulated
observations. It is also shown that the famous Rayleigh criterion (resolution =
wavelength / baseline) is inappropriate for interferometers as it does not
consider the reliability of the measurements.
