Spatial filter: Difference between revisions
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imported>Scott (New page: ===Purpose=== Image filtering ===Synopsis=== :xf = spatial_filter(x,win,options) ===Description=== Note that to allow robust statistics the filter is based on a moving window (or box),...) |
imported>Neal |
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===Purpose=== | ===Purpose=== | ||
Image filtering | Image filtering based on convolution (and deconvolution) | ||
===Synopsis=== | ===Synopsis=== | ||
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===Description=== | ===Description=== | ||
====Inputs==== | ====Inputs==== | ||
* '''x''' = image data class 'double' or 'dataset'. If 'dataset' it must x.type=='image'. If 'double' it must be | * '''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==== | ||
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::'gaussian' - (win) corresponds to the std in the Gaussian distribution. | ::'gaussian' - (win) corresponds to the std in the Gaussian distribution. | ||
::'box' - (win) is the number of x- and y- channels. | ::'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=== | ===See Also=== | ||
[[box_filter]], [[ | [[box_filter]], [[line_filter]], [[savgol2d]] | ||
Latest revision as of 11:21, 21 August 2009
Purpose
Image filtering based on convolution (and deconvolution)
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.
- 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).