If the noise model can take into account the neighbouring pixels correlations this is even better, because information about the noise in one pixel gives away information about noise in close by pixels.

The worst case is when the noise is uncorrelated. Think about the other extreme, when noise in all pixels has correlation 1, i.e. it's equal. Then it is extremely easy to remove.

(BTW, I would be very interested to the paper you are talking about, if you can find it :) )