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
avg an nxn average filter.
findEdges a 3x3 edge finding filter.
Gauss a nxn Gaussian blur filter.
GradXX 3x3 gradient filters with XX representing the two letters of the compass gradient direction.
hybrid median a 5x5 ranked median filter.
max sets the pixel value to the maximum value in the filter's size neighborhood.
median a nxn median filter.
min sets the pixel value to the minimum value in the filter's size neighborhood.
ranked median user-defined ranked median filter.
sharpen a 3x3 sharpening convolution filter.
thin calculates image thining using neighborhood maps.




Igor Pro 9

Learn More

Igor XOP Toolkit

Learn More

Igor NIDAQ Tools MX

Learn More