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2D filtering
Description
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).
Importance
High