What does standard deviation of noise 1 mean? In theory of noise,Typically, the standard deviation of noise has 1 and mean has 0.
I think that the reason of noise mean has 0 that we can assume that all noise signal go to zero when we sum it all.
But I can't understand standard deviation of noise has 1 in image noise.
They are always 0~255. There is no negative value.

What does zero-mean noise mean?

Especially, but I have got an idea as following. I can't catch quite a bit. So would you please let me know how to illustrate following answer?

"for an image intensity which is always non-negative, you can simply modify your added N(0,1) standard gaussian noise in 3 steps: 1-scale it so that your noise has whatever std you wish. 2-add whatever proper mean you need such as 128. 3-clip the noise for below zero and above 255 samples. This is simple bt It will help you get what you need at least initially."
Your question is too basic, and it gives the impression that you haven't done enough study of the subject on your own. I'll provide a few hints:


To understand noise, you first need to specify its probability distribution. I assume you're refering to Gaussian noise in your question, but not all noise is Gaussian. In particular, in digital capture of an image, there are several kinds of noise present.
Gaussian noise is specified by its mean and its variance ($\sigma_n^2$) or its standard deviation ($\sigma_n$). The physical interpretation of the variance is that it is related to the noise power. There is nothing special about $\sigma_n=1$.
Most computer random generators (for instance, Matlab's randn) generate samples of a Gaussian random variable with zero mean and variance 1. It is easy to convert this random variable to another Gaussian random variable with any specified mean and variance.
You need to understand the effects of clipping Gaussian noise. Think about a sensor photosite being pulled or pushed beyond its dynamic range by the noise. However, as I said in another answer, the resulting noise is no longer Gaussian.