  We present an application of the Richardson-Lucy algorithm to the analysis of
color-magnitude diagrams by converting the CMD into an image and using a
restoring point spread function function ({\it psf}) derived from the known,
often complex, sources of error. We show numerical experiments that demonstrate
good recovery of the original image and establish convergence rates for ideal
cases with single gaussian uncertainties and poisson noise using a $\chi^2$
statistic. About 30-50 iterations suffice. As an application, we show the
results for a particular case, the Hipparcos sample of the solar neighborhood
where the uncertainties are mainly due to parallax which we model with a
composite weighted gaussian using the observed error distributions. The
resulting psf has a slightly narrower core and broader wings than a single
gaussian. The reddening and photometric errors are considerably reduced by
restricting the sample to within 80 pc and to M_V \leq 3.5. We find that the
recovered image, which has a narrower, better defined main sequence and a more
clearly defined red giant clump, can be used as input to stellar evolution
modeling of the star formation rate in the solar vicinity and, with more
contributing uncertainties taken into account, for general Galactic and
extragalactic structure and population studies.
