Spatial filter

From Eigenvector Research Documentation Wiki
Jump to navigation Jump to search


Image filtering based on convolution (and deconvolution)


xf = spatial_filter(x,win,options)



  • x = image data class 'double' or 'dataset'. If 'dataset' it must x.type=='image'. If 'double' it must be MxNxP (P can = 1). M pixels in the X-direction (vertical in the image) and N pixels in the Y-direction (horizontal in the image).
  • win = a 1 or 2 element vector of odd integers corresponding to the window width of the box filter. If scalar, (win) is set to win = [win win]. See options.psf below for additional information.


  • xf = Filtered image class 'dataset'.


options = a structure array with the following fields:

  • algorithm: [ {'gaussian'} | 'box'] Point source function for filtering.
'gaussian' - (win) corresponds to the std in the Gaussian distribution.
'box' - (win) is the number of x- and y- channels.
  • conv: [ {'convolve'} | 'deconvolve' ] Governs the algorithm and tells it to convolve with the point source function given in (options.psf) or deconvolve. If 'deconvolve', then (options.reg) is used.
  • reg: {1e-6} regularization parameter (this parameter is used for ridging in the deconvolution algorithm).

See Also

box_filter, line_filter, savgol2d