  A method for spatial deconvolution of spectra is presented. It follows the
same fundamental principles as the ``MCS image deconvolution algorithm''
(Magain, Courbin, Sohy, 1998) and uses information contained in the spectrum of
a reference Point Spread Function (PSF) to spatially deconvolve spectra of very
blended sources. An improved resolution rather than an infinite one is aimed
at, overcoming the well known problem of ``deconvolution artefacts''. As in the
MCS algorithm, the data are decomposed into a sum of analytical point sources
and a numerically deconvolved background, so that the spectrum of extended
sources in the immediate vicinity of bright point sources may be accurately
extracted and sharpened. The algorithm has been tested on simulated data
including seeing variation as a function of wavelength and atmospheric
refraction. It is shown that the spectra of severely blended point sources can
be resolved while fully preserving the spectrophotometric properties of the
data. Extended objects ``hidden'' by bright point sources (up to 4-5 magnitudes
brighter) can be accurately recovered as well, provided the data have a
sufficiently high total signal-to-noise ratio (200-300 per spectral resolution
element). Such spectra are relatively easy to obtain, even down to faint
magnitudes, within a few hours of integration time with 10m class telescopes.
