Hi,
I did some additional research.
In essence, this image is a Q-Q plot
http://en.wikipedia.org/wiki/Q-Q_plot for a uniform distribution
http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29, i.e. when all values generated by the random process (here: CCD values) are equally likely within a certain range, the plot will be similar to a straight line. For distributions where values at the extremes become less likely (e.g. Poisson or normal distribution), the curve will always look more or less like an inverted S.
Basically, finding "outliers" always means that you have some idea how the data should be distributed, and filtering out those that do niot fit under this assumption. I think for CCD data, assuming a Poisson distribution
http://en.wikipedia.org/wiki/Poisson_distribution is a valid approach. So to further refine this approach, it may be a good idea to use a Q-Q plot for a poisson distribution instead of one for a uniform distribution.
Just some ideas, not practically tested....
Georg
