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#1
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Noise in uniform image: which metric?
Good morning,
I have a question related to a problem quite difficult for me. I'm involved in the task of calibrating the Correlated Double Sampling system for a CCD device. More in detail I have to select the best SR and SV values for a set of camera. In order to perform the task we expose the CCD to a uniform light, with controlled lux level and without any optic mounted on the device. By this way we expect to receive a uniform, flat image. My doubt is: which metric can better describe the noise in a "theoretically" uniform image? I supposed to use some metric related to DFT (in order to detect high and (with some more problem) low frequency noise) and may be something related to Haralick second order features (entropy, may be?). Then minimizing the obtained values, I should decide which Sr Sv is better for my camera. Is there someone that can give me some advice? Online I mainly found noise estimation techniques used at run time, and not with a test "flat" image. Any help or reference to online paper/book is greatly appreciated. Please note that the calibration setup should be performed for each one of the produced camera. |
#2
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Noise in uniform image: which metric?
On 2010-03-23, Giox wrote:
Good morning, I have a question related to a problem quite difficult for me. I'm involved in the task of calibrating the Correlated Double Sampling system for a CCD device. I wouuld advice you to get a copy of "Photon Transfer" by James Janesick. There is more detail than you probably want to know in that book. |
#3
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Noise in uniform image: which metric?
On 2010-03-23, Chris L Peterson wrote:
Unless something is very wrong with the camera design, the noise you see with a well illuminated sensor should be massively dominated by the Poisson distribution of the signal itself This is only the case in the absense of fixed pattern noise. |
#4
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Noise in uniform image: which metric?
Unless something is very wrong with the camera design, the noise you see
with a well illuminated sensor should be massively dominated by the Poisson distribution of the signal itself This is only the case in the absense of fixed pattern noise. Fixed pattern "noise" isn't noise, however. That depends on how you define noise. However, the OP is interested in calibrating his detector, and he won't find he has a poission distribution untill after he makes the correction for fixed pattern noise. You can trivially remove it by applying dark frames, and then you're left with actual noise. Fixed pattern noise (at least in the way I use the term) is removed via flats, not darks. |
#5
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Noise in uniform image: which metric?
In the world of CCDs, noise is true noise.
And yet, there exist effects called "noise", such as read noise, shot noise, and fixed pattern noise. If you want to spend time arguing that "fixed pattern noise" isn't noise, knock yourself out. |
#6
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Noise in uniform image: which metric?
On 24 Mar, 02:49, Greg Hennessy wrote:
In the world of CCDs,noiseis truenoise. And yet, there exist effects called "noise", such as readnoise, shotnoise, and fixed patternnoise. If you want to spend time arguing that "fixed patternnoise" isn'tnoise, knock yourself out. First thanks to both of you, your discussion was useful. Then, I'm sorry, I didn't replay to your posts since I had some personal problem and now I'm back at work. So thanks again for your ideas. I will look at the flat fielding technique in the suggested book, I think it could help me finding a noise estimation approach. Any further hint is appreciated. Gio |
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