- #1
cp05
- 11
- 0
Hi guys,
I've been struggling with this concept for a few days now.
I have this *good* image that I am blurring (using a gaussian blur), then adding some small percentage of gaussian white noise to it. Then from that image, I want to compute the naive solution (just assuming B=AX and solving for X...where B is the blurred image I created and X is the *good* image, with A being the matrix that takes into account this gaussian point spread function).
I have a handy dandy textbook that says that if I use periodic boundary conditions (which I am), then I can just use the point spread function and don't ever have to construct A (yay!). I do this by computing the eigenvalues of A using fast Fourier transforms (not sure how those work either...but I guess that's a different question for a different time), then using the inverse fast Fourier transform to solve for X using the blurred matrix and those eigenvalues of A.
All great!
By playing around with the radius of my gaussian blur...I notice that at larger gaussian blur radii (ad same % of white noise), my resulting naive X solution has a lot more noise! So I was wondering if anyone can help me figure out why this happens. Why am I getting more noise in my solution with larger radii of blur...because I am not changing the % of white noise at all!
If someone could help, or point me in the right direction, I would be ever grateful.
Thanks!
I've been struggling with this concept for a few days now.
I have this *good* image that I am blurring (using a gaussian blur), then adding some small percentage of gaussian white noise to it. Then from that image, I want to compute the naive solution (just assuming B=AX and solving for X...where B is the blurred image I created and X is the *good* image, with A being the matrix that takes into account this gaussian point spread function).
I have a handy dandy textbook that says that if I use periodic boundary conditions (which I am), then I can just use the point spread function and don't ever have to construct A (yay!). I do this by computing the eigenvalues of A using fast Fourier transforms (not sure how those work either...but I guess that's a different question for a different time), then using the inverse fast Fourier transform to solve for X using the blurred matrix and those eigenvalues of A.
All great!
By playing around with the radius of my gaussian blur...I notice that at larger gaussian blur radii (ad same % of white noise), my resulting naive X solution has a lot more noise! So I was wondering if anyone can help me figure out why this happens. Why am I getting more noise in my solution with larger radii of blur...because I am not changing the % of white noise at all!
If someone could help, or point me in the right direction, I would be ever grateful.
Thanks!