Emil Martinec
New member
I've been having some fun poking around in the noise of Canon DSLR's, and found something puzzling. I got to the point of measuring the Poisson noise. I took pairs of shots of a cloudless sky with my 20D and 100-400L @400mm, f7.1, ISO 100; the camera in manual focus at MFD with IS off. Shutter speeds increased from well beyond saturation to 1/8000 sec, in 1/3 stops. I then analyzed the raw data in IRIS, separating the CFA into g2, b, r g1 channels. Taking the difference of the two exposures for the g2 subarray, I recorded the standard deviation and the average raw value (subtracting the black point of 128). I was able to reproduce classic Poisson noise (the first plot below), with the best fit curve giving read noise of 2.7 ADU and 11.7 electrons/ADU. The latter is a little low relative to other measurements, which arrive at a typical value of 12.4 electrons/ADU, but not far off.
More puzzling is that I did the same comparison by subtracting the two green channels of a single image (g2-g1) and using the standard deviation as a measure of the noise. This came out rather higher than the difference of two images taken in quick succession. Subtracting in quadrature the Poisson noise of the difference of two images from the noise obtained from (g2-g1) of a single image, and taking the square root, yields the second plot below -- within experimental error quite linear with respect to exposure. Note that this residual noise is quite comparable in magnitude to the Poisson noise.
Again, the first plot below is the rms fluctuation in ADU of the difference of two successive images, versus signal in ADU. The second plot is the rms fluctuation in ADU of the difference of the two green subarrays of the Bayer pattern of a single image from the pair, versus signal in ADU, after subtracting off the Poisson noise.
The approximate best fit to the noise in ADU is (here x is the average signal in ADU with blackpoint subtracted)
noise = Sqrt[7.1 + .086 x + (.0058 x)^2]
So my question is what is the potential source of this noise, and is this something well known that I have overlooked. What is needed is a noise whose amplitude is proportional to signal, and that drops out if take the difference of two images. This rules out quantities that vary from image to image, since such an effect produces noise in the difference more than in (g2-g1) of a single image. Things I can think of:
1. My sensor is incredibly dirty at the pixel level, so that the transmissivity of incident light to the sensels varies on the .6% level between pixels diagonally adjacent to one another, across the entire sensor.
2. The gain applied at individual photosites varies on the .6% level, so that (g2-g1) of a single image has a variance above and beyond that due to Poisson noise.
Both these effects would produce a "noise" or variance in the recorded exposure that is proportional to exposure, as I have observed (and as distinct from Poisson noise, which varies as the square root of exposure). Any other suggestions or illuminating comments are most welcome.
More puzzling is that I did the same comparison by subtracting the two green channels of a single image (g2-g1) and using the standard deviation as a measure of the noise. This came out rather higher than the difference of two images taken in quick succession. Subtracting in quadrature the Poisson noise of the difference of two images from the noise obtained from (g2-g1) of a single image, and taking the square root, yields the second plot below -- within experimental error quite linear with respect to exposure. Note that this residual noise is quite comparable in magnitude to the Poisson noise.
Again, the first plot below is the rms fluctuation in ADU of the difference of two successive images, versus signal in ADU. The second plot is the rms fluctuation in ADU of the difference of the two green subarrays of the Bayer pattern of a single image from the pair, versus signal in ADU, after subtracting off the Poisson noise.
The approximate best fit to the noise in ADU is (here x is the average signal in ADU with blackpoint subtracted)
noise = Sqrt[7.1 + .086 x + (.0058 x)^2]
So my question is what is the potential source of this noise, and is this something well known that I have overlooked. What is needed is a noise whose amplitude is proportional to signal, and that drops out if take the difference of two images. This rules out quantities that vary from image to image, since such an effect produces noise in the difference more than in (g2-g1) of a single image. Things I can think of:
1. My sensor is incredibly dirty at the pixel level, so that the transmissivity of incident light to the sensels varies on the .6% level between pixels diagonally adjacent to one another, across the entire sensor.
2. The gain applied at individual photosites varies on the .6% level, so that (g2-g1) of a single image has a variance above and beyond that due to Poisson noise.
Both these effects would produce a "noise" or variance in the recorded exposure that is proportional to exposure, as I have observed (and as distinct from Poisson noise, which varies as the square root of exposure). Any other suggestions or illuminating comments are most welcome.
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