  A non-parametric smoothing method is presented that reduces noise in
multi-wavelength imaging data sets. Using Principle Component Analysis
(hereafter PCA) to associate pixels according to their $ugriz$-band colors,
smoothing is done over pixels with a similar location in PCA space. This method
smoothes over pixels with similar color, which reduces the amount of mixing of
different colors within the smoothing region. The method is tested using a mock
galaxy with signal-to-noise levels and color characteristics of SDSS data. When
comparing this method to smoothing methods using a fixed radial profile or an
adaptive radial profile, the chi^2-like statistic for the method presented here
is smaller. The method shows a small dependence on input parameters. Running
this method on SDSS data and fitting theoretical stellar population models to
the smoothed data of the mock galaxy and SDSS data, shows that the method
reduces scatter in the best-fit stellar population analysis parameters, when
compared to cases where no smoothing is done. For an area centered on the star
forming region of the mock galaxy, the median and standard deviation of the
PCA-smoothed data is 7 Myr (+/- 3 Myr), as compared to 10 Myr (+/- 1 Myr) for a
simple radial average, where the noise-free true value is 7.5 Myr (+/- 3.7
Myr).
