So now let's begin section eight, where we take a look at convolutions blurring and sharpening images.
So you already have this open, so just double click and open it yourself and nose move up to the top.
So the first thing we're going to do is just basically load off our libraries and download images again
and a list.
I'll discuss the lesson.
So what we're going to do, we're going to look at what convolutional operations are.
And then we're going to apply blurring the noise thing and sharpening to our images.
So firstly, convolutions are basically an operation where we actually composition is basically a mathematical
operation performed on two functions that produces a tiered function, which is typically a modified
version of one of the original functions.
So that's a bit of a mouthful, but basically what we're doing here.
We're just getting an output image.
We're basically we're creating a kernel kernel basically is what we call what we're calling a tree by
tree matrix of 1s here, and we just divide it by nine so that we can actually scale it back down by
a factor of one of the nine.
And what we do now is I'll explain this to you after lattices and actually so we don't affect the brightness
of the image just so you know.
So whatever factor, whatever how, however, many added elements are in this matrix attribute, you
would have nine elements.
You divide it by nine.
That's a way to keep the brightness consistent.
So what we do know, we actually use this function called see v2.0 filter to involve the kernel with
an image.
So controlling basically is an operation here where we just operate on the image with this function
and we get the output, which is a split image.
And what I'm going to show you now is with different sized kernels, the larger the kernel of the more
blue.
So let's run this function, the more blue we apply.
I should see.
So you can see the tree by tree blue.
It's not that that much.
I mean, it is blue blurrier than the original, but not significantly so.
However, when you use a seven by seven kill, you can see it's actually quite a lot more very than
this one and committed the original.
So that's good.
So let's take a look at No.
Some.
So that was that was a convolutional way of actually applying a blur.
However, let's actually look at some of the built in the three methods that open.
CBS Serpent TV has this function called CDK2 Dark Blue, which is actually averaging done by involving
the image was a normalized box filter, and you can read about it a bit more if you want to get a deeper
understanding online.
And the open TV documentation on Wikipedia has good entries sometimes for these things.
So we're going to apply to Blue with a five by five kernel here.
What we're going to do now is going to apply Gaussian Blue Ocean Blue and into medium blue.
So it's run.
All of these have all of these are going to be done at five by five kernels.
So you can see averaging, which is a standard blue Gaussian blue and media medium blurring which looks,
which looks pretty good as well.
You don't actually see a huge difference in these blurring methods unless you actually inspect up close,
which you wouldn't be doing here.
So now let's take a look at this bilateral filter.
What is this function?
Will this function is actually a noise removal function in open TV?
These are these are the parameters of ticks, and we're going to go through all of this in detail.
We're just going to use some of the default parameters here.
Well, for properties, these are properties that actually produce some fairly good results.
We're going to use nine foot diameter.
That's each Pixel neighborhood we're looking at.
We're going to use Sigma Color 75 and Sigma Space 75 as well.
So let's take a look and see how this works.
And you can see we took the original image and we applied our bilateral filter, and it gives us this
output here, which is pretty cool.
It's not really that useful when depending on the application, if you want to do.
But it's a good implementation of noise removal.
So now let's look at some more noise removal functions.
So these are fast.
And I mean, it's not.
The noisy colored multi-colored is the different algorithms for different purposes.
So let's take a look at the colored one.
So we look at original image and then we applied this year, and this is our original image here, and
we're going to apply the first thing to it.
And you can see it actually looks roughly similar, maybe I should have used that image with more noise,
but you can definitely see that it actually looks a bit more a bit more like artificially enhanced.
Like you can see, it doesn't have as much like this looks a lot cleaner than this one.
The edges lives are a little bit smoother, so it does clean it up technically a bit, but maybe it
looks a bit too artificial.
So what about sharpening or sharpening is applied very similarly to blurring.
You can use the seams quietude of filter image filter 2D to involve it with a sharpening matrix here.
This is what a sharpening matrix looks like is nine in the middle.
That's the value for the number of number of elements in this matrix, and we have minus one all around.
And surprisingly, by doing this what you do, you get a much more sharpened image.
Sharpening images basically means you're enhancing the edges a bit more cities.
Everything looks a bit more highly HDR type effect where everything looks a bit more like a little more
visible.
Or I'm not sure what it would want to says, but you can see it.
A picture is worth a thousand words, so you can see what the difference was something which only makes
it.
So that's it for this lesson.
I hope you enjoyed it, and it's going to move on to the ninth lesson, which is troubling by the issued
an adaptive threshold.
Thank you.