  The CLEAN deconvolution algorithm has well-known limitations due to the
restriction of locating point source model components on a discretized grid. In
this letter we demonstrate that these limitations are even more pronounced when
applying CLEAN in the case of Rotation Measure (RM) synthesis imaging. We
suggest a modification that uses Maximum Likelihood estimation to adjust the
CLEAN-derived sky model. We demonstrate through the use of mock one-dimensional
RM synthesis observations that this technique shows significant improvement
over standard CLEAN and gives results that are independent of the chosen image
pixelization. We suggest using this simple modification to CLEAN in upcoming
polarization sensitive sky surveys.
