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So let's quickly discuss sharpening sharpening as you imagine is the opposite of blurring and it actually

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strengthens strengthens and emphasizes the edges image as you can see in this example of sharpening

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here.

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The edges here are normal.

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What you would see with your own eyes however in this image all the edges here are much more pronounced

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physical horizontal.

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Are all the rooftops everywhere.

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So Templeman sharpening.

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We actually have to change our kernel and actually use the CV to filter to the function.

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So kernel for sharpening actually looks quite different here.

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However as you can tell it still seems to one that we we normalize our image other ways if it didn't

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normalize to one your image would be brighter or darker respectively.

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So let's run this simple sharpening example in our code.

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So again we look at image and then we create or sharpening Clennell says he saw before we have minus

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ones and all rows here and columns except indeed directions where we have nine.

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So if you would have some other elements in this matrix you'd actually get one which is exactly what

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we wanted.

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Which means it's normalized and to run or implement a sharpening function we use C-v to fill the 2d

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implemented shopping effect.

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So we take the shopping kernel and the input image we run it and we get a shop and image here.

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So let's take a look

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and there we go.

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This is our original image and this is a much sharper image.

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And as you can see just like the images we saw on the slide all the edges are much more pronounced.

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So it looks a bit artificial but you can play around with it couldn't.

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Metrics and trying it when shopping matrics They always to actually get a much nicer looking sharpening

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sharpened image here.