  We propose the use of entropy, measured from the spatial and flux
distribution of pixels in the residual image, as a potential diagnostic and
stopping metric for the CLEAN algorithm. Despite its broad success as the
standard deconvolution approach in radio interferometry, finding the optimum
stopping point for the iterative CLEAN algorithm is still a challenge. We show
that the entropy of the residual image, measured during the final stages of
CLEAN, can be computed without prior knowledge of the source structure or
expected noise levels, and that finding the point of maximum entropy as a
measure of randomness in the residual image serves as a robust stopping
criterion. We also find that, when compared to the expected thermal noise in
the image, the maximum entropy of the residuals is a useful diagnostic that can
reveal the presence of data editing, calibration, or deconvolution issues that
may limit the fidelity of the final CLEAN map.
