Filtering

Spatial Domain Linear Filters

Applies the Canny edge-detection method to an image

Performs smoothing on 1D, 2D or 3D image arrays

Computes a 1D or 2D spatial Gaussian filter kernel

Performs 1D, 2D or 3D convolution on signals, images, and volumes

Creates a spatial filter object, given a kernel and other appropriate fields

Reads an ASCII text or XDR file depending on whether the file contains a spatial or spectral filter (respectively)

Saves a 2D convolution kernel to an ASCII text file

Generates a 2D Butterworth or ideal lowpass, bandpass, bandstop or highpass spatial frequency domain filter

Generates a 3D ideal notch spatial frequency domain filter

Computes and applies a parametric Wiener filter to an image that is either in the spatial or the spatial frequency domain

Performs 1D, 2D or 3D adaptive double-window-modified trimmed mean filter

Performs 1D, 2D or 3D adaptive minimum mean-squared error filtering

Performs nonlinear filtering operations on 1D, 2D, or 3D image arrays

Computes one of several different data windows: Blackman, Chebyshev, Hamming, Hanning, Kaiser, rectangular, or triangular