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The kernel appears to work by adding together a kernel that computes the amount by which the central pixel deviates from its surrounding neighborhood to the original image. If the pixels in the neighborhood have the same value, the deviation is zero; but if the central pixel is higher or lower than the surrounding ones, the difference is multiplied by 8 and added to the original image. As a result, small deviations in the central pixel's deviation from the norm are enhanced.

Alternatively, you can argue that the kernel works by subtracting the average of the central pixel's neighborhood from a large multiple of the pixel:

Original 5x5 Gaussian

Image Unsharp Mask

Original 5x5 Gaussian

Image Unsharp Mask

Figure 14.9 At large scale, you can see how sharpening changes individual pixels. Those brighter than their neighbors become brighter, and pixels darker than their neighbors become darker; but the overall image brightness remains the same. As a result, low-contrast features become easier to see.

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