  CLEAN, the commonly employed imaging algorithm in radio interferometry,
suffers from a number of shortcomings: in its basic version it does not have
the concept of diffuse flux, and the common practice of convolving the CLEAN
components with the CLEAN beam erases the potential for super-resolution; it
does not output uncertainty information; it produces images with unphysical
negative flux regions; and its results are highly dependent on the so-called
weighting scheme as well as on any human choice of CLEAN masks to guiding the
imaging. Here, we present the Bayesian imaging algorithm resolve which solves
the above problems and naturally leads to super-resolution. We take a VLA
observation of Cygnus~A at four different frequencies and image it with
single-scale CLEAN, multi-scale CLEAN and resolve. Alongside the sky brightness
distribution resolve estimates a baseline-dependent correction function for the
noise budget, the Bayesian equivalent of weighting schemes. We report noise
correction factors between 0.4 and 429. The enhancements achieved by resolve
come at the cost of higher computational effort.
