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.

Some of the MatrixFilter operators
avgan nxn average filter.
findEdgesa 3x3 edge finding filter.
Gaussa nxn Gaussian blur filter.
GradXX3x3 gradient filters with XX representing the two letters of the compass gradient direction.
hybrid mediana 5x5 ranked median filter.
maxsets the pixel value to the maximum value in the filter's size neighborhood.
mediana nxn median filter.
minsets the pixel value to the minimum value in the filter's size neighborhood.
ranked medianuser-defined ranked median filter.
sharpena 3x3 sharpening convolution filter.
thincalculates image thining using neighborhood maps.




Igor Pro 8

Learn More

Igor XOP Toolkit

Learn More

Igor NIDAQ Tools MX

Learn More