Localized filters modify the value of each image pixel based on the value of pixels in its neighborhood. Localized filters are sometimes implemented as a convolution. Convolution filtering is a method for modifying the appearance of an image by convolving its pixel values with a transformation kernel. When the kernel is small it is more efficient to use the built-in operation MatrixConvolve. When the convolution kernel is relatively large it is more efficient to compute the convolution using the fast Fourier transform (FFT). A number of typical convolution kernels are implemented in the operation MatrixFilter which also contains a few localized (neighborhood) operators.
|an nxn average filter.
|a 3x3 edge finding filter.
|a nxn Gaussian blur filter.
|3x3 gradient filters with XX representing the two letters of the compass gradient direction.
|a 5x5 ranked median filter.
|sets the pixel value to the maximum value in the filter's size neighborhood.
|a nxn median filter.
|sets the pixel value to the minimum value in the filter's size neighborhood.
|user-defined ranked median filter.
|a 3x3 sharpening convolution filter.
|calculates image thining using neighborhood maps.
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