I am learning in an image processing course, and the professor did the following: As part of a derivation, has this:
What I do not understand, is how he was able to remove $r(i,j)$ to the 'outside'. I understand that it can be removed to the outside of the expectation since it is deterministic. What I do not understand is how he is able to remove it from the convolution.
TLDR: How did we go from $\mathbb{E}\Big[ \big[y(i,j) \star r(i,j) \big] y^*(i,j) \Big]$, to this: $r(i,j) \star \mathbb{E}\Big[ y(i,j) \ y^*(i,j) \Big]$ ?
Thanks.