No and welcome back. Let us see the wiener filter in action
once again using MATHLAB. As we have done before we start by
loading an image. This is the same image we have seen
before, this Saturn image, remember that was a color image.
So we're going to transform it into black and white image, the same operation as we
did before. Then we're going to add Gaussian noise,
as we see here, we have seen this before when we were showing the different types
of noise and we see here the variance. Now, are go and apply the wiener filter.
This is the operation that applies the wiener filter to this image that we have
just basically created by adding Gaussian noise.
Now before I show you the result of the Wiener filter I want to compare that with
a different filter. Basically, I want to compare it with a
local averaging. For that I'm creating a disk of radius
ten and then basically filtering the noise image with that disk.
This is basically, this operation is basically computing the
local average, weka window of radius ten.
Don't worry about this, this is just a, what, is telling MATHLAB
what to do at the boundary conditions. So basically, we're going to have, the
original image, we're going to have the noisy image, we're going to have the
blurry image, local averaging, and we're going to have the result of Wiener
filtering. So let's see the results.
Once again we're loading all the images. And this will help us to see how nice
wiener filtering can work. we see again the original image, that we
have seen in the past. This is the noisy image.
This is the result of Wiener filtering. And this is the result of blurring, or
local averaging. So see how nicely Wiener filtering has
been able to recover. Restore a lot of the original image,
starting from this noisy image. And it has done a much better job than
the local averaging. Let me move the image closer, and we can
see the difference. This is clearly a much sharper image than
what we obtained knowing basically, doing the local averaging.
So wiener as we see can do a fantastic job when we provide some information.
And in this case, actually, the wiener is estimating the information.
So this is the, basically the implementation that we saw
at the end of the previous video, the simplest implementation of the wiener
filtering and still able to do a great job in restoring the image, starting from
this noisy image. Then we once again, this was the
original, the noisy of course the wiener filter doesn't know this original images
starts from this noisy image and still operating that blurry version of it,
creates a really sharp version. So it's doing a really, really good job.
And we can see much better than the local averaging that blurs.
So by this we conclude that themma on the wiener victory.
Thank you very much.