why always structured Images are considered as 2D signals? I am dealing with 2D signals that are random. To be more specific my input is a matrix of 1's and 0's. Moreover, at any position (n1,n2) the probability of having a 1 or a 0 is same. One example is shown below



When I look at books dealing with 2D signal processing, everything is explained wrt to image processing. In my understanding, image is a very special kind of 2D signal where one can expect to see some relationship between the pixels like EDGES. Such features are useful when doing image restoration,etc.



These books start with defining 2D signals and system but explain everything wrt to image processing application which is a bit restrictive. 
Why is the case?
A 1D signal that is only noise with no structured feature to be seen is of no interest.

A 2D signal that is only noise or a superposition of uncorrelated 1D signal rows, with no 2D structured feature to be seen, is of no interest.