  One representation of possible errors in a grayscale image reconstruction is
as another grayscale image estimating potentially worrisome differences between
the reconstruction and the actual "ground-truth" reality. Visualizations and
summary statistics can aid in the interpretation of such a representation of
error estimates. Visualizations include suitable colorizations of the
reconstruction, as well as the obvious "correction" of the reconstruction by
subtracting off the error estimates. The canonical summary statistic would be
the root-mean-square of the error estimates. Numerical examples involving
cranial magnetic-resonance imaging clarify the relative merits of the various
methods in the context of compressed sensing. Unfortunately, the colorizations
appear likely to be too distracting for actual clinical practice, and the
root-mean-square gets swamped by background noise in the error estimates.
Fortunately, straightforward displays of the error estimates and of the
"corrected" reconstruction are illuminating, and the root-mean-square improves
greatly after mild blurring of the error estimates; the blurring is barely
perceptible to the human eye yet smooths away background noise that would
otherwise overwhelm the root-mean-square.
