  Compressive sampling is a new paradigm for sampling, based on sparseness of
signals or signal representations. It is much less restrictive than
Nyquist-Shannon sampling theory and thus explains and systematises the
widespread experience that methods such as the H\"ogbom CLEAN can violate the
Nyquist-Shannon sampling requirements. In this paper, a CS-based deconvolution
method for extended sources is introduced. This method can reconstruct both
point sources and extended sources (using the isotropic undecimated wavelet
transform as a basis function for the reconstruction step). We compare this
CS-based deconvolution method with two CLEAN-based deconvolution methods: the
H\"ogbom CLEAN and the multiscale CLEAN. This new method shows the best
performance in deconvolving extended sources for both uniform and natural
weighting of the sampled visibilities. Both visual and numerical results of the
comparison are provided.
