All linear convolution filters compute weighted averages of the neighboring input image pixels. The only differences between the various linear convolution filters are the size and shape of the neighborhood, and the specific weights used.

User Defined Filters

There are two types of user defined filters, Low-pass Filters and General User-defined. With these filters, you can specify the height and width of the filter neighborhood.

Low-pass Filters - A low-pass filter removes the high frequency noise with the resulting output being a smoother grid. There are three user-defined low-pass filters. Each of these three filters allows you to specify the size of the neighborhood. The width and height of the filter neighborhood must both be positive, odd numbers.

General User-defined Filter - The General User-defined (MxN) linear filter allows you to specify the height and width of the filter neighborhood and any combination of weights. The table in the center part of the window displays the neighborhood size, based on the number of Rows and Cols, along with the weights for each pixel node in the neighborhood. Click in a cell in the table to change the node's weight.

Predefined Filters

The predefined filters have a large collection of 3×3 filters.

- Low-pass Filters are also known as smoothing or blurring filters. These filters remove the high frequency variation.
- High-pass Filters are also known as sharpening or crispening filters. They have the opposite effect of blurring. They tend to remove the background variation and emphasize the local details.
- Order 1 Derivative Filters are used to find horizontal and vertical edges.
- Order 2 Derivative Filters are another set of edge enhancement filters.
- Shift and Difference Filters are the two simplest horizontal and vertical differential operators.
- Gradient Directional Filters compute and return the directional derivatives in each of the eight compass directions.
- Embossing Filters identify and enhance edges aligned in one of the eight compass directions.

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