How does salt and pepper noise occurs in an image
Salt-and-pepper noise is a form of noise sometimes seen on images. It presents itself as sparsely occurring white and black pixels.

In another words ( in the sense of pixels), salt and pepper noise means that are high frequencies, so for salt noise the values of this noise type is high (255 ... 200), and for the pepper noise the values of this noise type is low (5 ... 0). 

To salt noise there are good filters like harmonic mean filters
to salt and pepper noise.

$$
   \hat{f}(x,y) = \frac{mn}{\sum_{(s,t) \in S_{xy}}\frac{1}{g(s,t)} }
   $$

There are the contra harmonic filter that depends of a $Q$ argument, e.g., for salt noise $Q$ should be negative and for pepper Q should be positive.

$$
   \hat{f}(x,y) = \frac{\sum_{(s,t) \in S_{xy}}g(s,t)^{Q+1}}{{\tiny\sum_{(s,t) \in S_{xy}}}g(s,t)^{Q} }
   $$

Another aproach is the order statistics filters, e.g., $\operatorname{max}$
 filter are good for pepper noise, and $\operatorname{min}$ filter are good for salt noise.

$$ \hat{f}(x,y) = \operatorname{max}_{(s,t)\in S_{xy}}{g(s,t)}$$
$$ \hat{f}(x,y) = \operatorname{min}_{(s,t)\in S_{xy}}{g(s,t)} $$

And another one is alpha trimmed filter that also are a order statistics filter type.

$$ \hat{f}(x,y) = \frac{1}{mn-d}\sum_{(s,t) \in S_{x,y}} g_r(s,t)$$

Beside this there are Adaptive Mean Filter.