Which domain used for denoising additive and multiplicative noises
I want to know what is the difference between additive noise,
  multiplicative noise.. In what domain these noises are handled to
  remove?


Additive noise and multiplicative noise are just models of how noise corrupts our data. 

One very common model is the additive noise model, where we have our 'true' data vector $s[n]$, (which we are trying to ascertain), being corrupted by a noise vector, $v[n]$. What we are given, is $x[n]$, where:

$$
x[n] = s[n] + v[n]
$$ 

This is called the 'additive' noise model, because as you can see, noise is added to our true signal, giving us $x[n]$. There are many ways in which we can remove this noise in an additive model, such as filtering, (which is a form of averaging if you like). This is a very common type of noise model. If I am talking, and you are talking over me, your voice can be modeled as additive to my voice. You voice would be additive 'noise', that is added to my voice, which in this vain example is the 'true' signal, (although this would be disputed in a heated argument between two people). In a more objective example, thermal noise from a microphone's electronics can also be modeled as an additive noise, which is added to a voice signal that it has received. Many things can be modeled as additive types of noises.

Multiplicative noise on the other hand is still a model, but in this model our true data samples are being multiplied by noise samples, like so:

$$
x[n] = s[n]v[n]
$$

One common way to remove multiplicative noise is to actually transform it into an additive model, and then apply everything we know from the additive noise reduction field. We can do this easily via taking the logarithm of the signal, filtering, then inverse log transform. Thus we can do:

$$
x[n] = s[n]v[n] \implies \log(x[n]) = \log(s[n]v[n]) = \log(s[n]) + \log(v[n])
$$

At this point we have an additive model once more. Now, we can filter as we normally might, to remove or reduce $\log(v[n])$, and then simply take $\log^{-1}$ of the result, yielding an estimate of $s[n]$, our true signal. 


  I am interested particularly in image dataset


One example of multiplicative noise is in illumination differences between images, which is solved in the above way. The non-uniform illumination across an image can be modeled as a pixel-by-pixel multiplication of the image by an illumination mask. This is also known as homomorphic filtering by the way. Anytime you can model a corrupting phenomenon as multiplying your clean data signal, you can use this multiplicative model.