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in Digital Image Processing restoration ,from my understanding after reading articles , it's seems that the noise must be dominant in the histogram of the image , which is make it possible , to get the mean and variance of the noise from the histogram it self.
My question is if I have a little Gaussian noise in the image , which i means that the noise is not dominant in the image , it's seems that it's wrong to extract the mean and variance from the image , because if we do this , we finding the mean and variance of the distorted image it self which is included the noise , yields that we affect on the original image if we use the model , which is included the model of the noise and the original image together, So how could we avoid this problem ?

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  • $\begingroup$ Compute the median. It is a robust mean. For the latter, use MAD (median absolute deviations), which is a robust deviation estimator. $\endgroup$ – Tolga Birdal Dec 30 '14 at 13:16
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If the noise is a constant background/remains in the same place, you could take various photos in the absence of any light to create an image for background subtraction. My image processing expertise is limited to grayscale but i would imagine the same could be done for RGB. Produce several background images, from that make an average/low pass filter. Then in your desired programming language do some simple matrix manipulation.

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