# Why doesn't noise depend on exposure time?

In the post Why do cameras use a single exposure rather than integrating across many very quick reads? an important question was if the noise depends on the exposure time or not. By noise I mean the absolute noise (N), not the relative noise (S/N). I was convinced about this correlation because I was thinking that the noise is accumulated during the exposure time, so if it is longer the noise would be higher.

To test this hypothesis I tried to shoot black images with the lens cap on the lenses, in such a way to have S=0. I am using Canon 600D, ISO100 and f/3.5

I wrote a python script to compute the mean value averaging over all the pixel with a simple mean or with a quadratic mean (sqrt(sum_i (R_i^2)) where R_i is the ith-pixel considering only the red channel.

I used jpeg, because I'm not able to read raw from python. Using jpeg the resolution of the noise is quite small (1/256), but I have obtained interesting results.

Here the plot: on the y-axis the noise, on the x-axis the exposure time.

it seems that the noise is quite constant up to exposure of 1s, then it start to increase

I have also tried to shot with ISO3200, here the results:

as expected the noise is much bigger (~10 times) but the behaviour with exposure time is similar (probably it raise a bit faster).

Why this behaviour?

Why is the green channel more noisy?

• I suspect you're seeing more noise in green because there are twice as many green photosites. (This is another area where you'd be better off interpreting the RAW. Try this for RAW processing with Python....) – mattdm Jan 26 '13 at 2:21