Spatial filter: Difference between revisions
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====Inputs==== | ====Inputs==== | ||
* '''x''' = image data class 'double' or 'dataset'. If 'dataset' it must x.type=='image'. If 'double' it must be ''M''x''N''x''P'' (''P'' can = 1). M pixels in the X-direction (vertical in the image) and N pixels in the Y-direction (horizontal in the image). | * '''x''' = image data class 'double' or 'dataset'. If 'dataset' it must x.type=='image'. If 'double' it must be ''M''x''N''x''P'' (''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]. | * '''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. | ||
====Outputs==== | ====Outputs==== | ||
Revision as of 11:09, 21 August 2009
Purpose
Image filtering based on convolution
Synopsis
- xf = spatial_filter(x,win,options)
Description
Inputs
- 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.
Outputs
- xf = Filtered image class 'dataset'.
Options
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.