3 ms·

yes, but binning pixels is inferior to averaging because the noise in adjacent pixels is correlated. (I had a nice paper about that, still looking for it)

• ot 15y ago
  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 :) )
  • av500 15y ago
    but the problem is that you do not know what is signal and what is noise. If you have two pixels with correlated noise, you have in the extreme case s_1 + n and s_2 + n, so averaging that gives you (s_1 + s_2) / 2 + n. If the noise is uncorrelated, then it might be positive in one pixel and negative in the others, so it might cancel itself out. In the extreme case you would have s_1 + n_1 and s_2 + n_2 and (n_1 + n_2) / 2 < n. Thus you have reduced the noise.