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Many
image processing operations are implemented using a *filter
(or kernel),
*which is applied to each pixel in an image.

In 2D filtering, the filter is simply a matrix of values that describe how to transform a pixel by replacing it with the sum of products between the values of nearby pixels and the filter weights.

For example, consider the following 3x3 filter, and a set of pixel values (grayscale intensities).

To apply the filter, we simply multiply corresponding elements together and sum the results.

In this case, the value would be (25 + 27 + 2*29 + 31 + 33) = 174

Typically the image is much larger than the filter. The filter is moved through the image to produce filtered outputs, which are stored in a separate buffer.

The filter can be any size (NxM matrix).

In 2D filtering, the filter is simply a matrix of values that describe how to transform a pixel by replacing it with the sum of products between the values of nearby pixels and the filter weights.

For example, consider the following 3x3 filter, and a set of pixel values (grayscale intensities).

To apply the filter, we simply multiply corresponding elements together and sum the results.

In this case, the value would be (25 + 27 + 2*29 + 31 + 33) = 174

Typically the image is much larger than the filter. The filter is moved through the image to produce filtered outputs, which are stored in a separate buffer.

The filter can be any size (NxM matrix).

High

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