Box filter: Difference between revisions
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imported>Scott (New page: ===Purpose=== Image filtering ===Synopsis=== :xf = box_filter(x,win,options) ===Description=== Note that to allow robust statistics the filter is based on a moving window (or box), and...) |
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===Purpose=== | ===Purpose=== | ||
Image filtering | Image or matrix (N-way) filtering (spatial filtering with a moving window) | ||
===Synopsis=== | ===Synopsis=== | ||
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===Description=== | ===Description=== | ||
BOX_FILTER is a spatial filter that filters and despises based on a moving window (or box). It tends to be slow compared to filter methods that can be vectorized. | |||
====Inputs==== | ====Inputs==== | ||
* '''x''' = | * '''x''' = MxNxP data class 'double' or 'dataset'. If 'dataset' it must x.type=='image' (see BUILDIMAGE). If 'double' it must be MxNxP (P can = 1 or N-way arrays). | ||
* '''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]. | ||
====Outputs==== | ====Outputs==== | ||
* '''xf''' = Filtered image class 'dataset'. | * '''xf''' = Filtered image class 'dataset' or matrix (or N-way array) class 'double.' | ||
===Options=== | ===Options=== | ||
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options = a structure array with the following fields: | options = a structure array with the following fields: | ||
* '''algorithm''': [ {'mean'} | 'median' | 'max' | 'min' | 'meantrimmed' | 'mediantrimmed' ] governs filter method. | * '''algorithm''': [ {'mean'} | 'median' | 'max' | 'min' | 'despike' | 'meantrimmed' | 'mediantrimmed' ] governs filter method. | ||
* '''ntrim''': when algorithm = 'meantrimmed' or 'mediantrimmed', (ntrim) is the input (n) to the functions MEANTRIMMED or MEDIANTRIMED {default = 2}. | * '''window movement''': [ {'bands'} | 'rectangle' ], 'bands' moves vertically then horizontally over a single window across the image or slab, and 'rectangle' moves a rectangular window across the image or slab. | ||
despike parameters | |||
* '''tol''': { [] } if empty then despike uses the std(median(x)) ''within each window'' to define the tolerance within the window. If tol>0 scalar, then tol defines the tolerance ''for all the windows''. If tol<=0, then tol is estimated by the mean absolute deviation of madc(x.data(:)) and is the tolerance used for all the windows (see MADC). | |||
* '''dsthreshold''': [ {2} ] Threshold used for the 'despike' algorithm. In contrast to replacing all values with the meidan (e.g., for options.algorithm = 'median'), 'despike' replaces only values outside |x-median(x)|>options.dsthreshold*options.tol with the median. (see options.tol) | |||
* '''trbflag''': [ {'middle'} | 'bottom' | 'top' ] top-or-bottom flag. For trbflag = 'middle' the filter replaces values outside |x-median(x)|>dsthreshold*options.tol) with the median. For trbflag = 'bottom' the filter replaces values outside (x-median(x))>dsthreshold*options.tol with the median. For trbflag = 'top' the filter replaces values outside (median(x)-x)>dsthreshold*soptions.tol with the median. | |||
ntrim parameters | |||
* '''ntrim''': when algorithm = 'meantrimmed' or 'mediantrimmed', (ntrim) is the input (n) to the functions MEANTRIMMED or MEDIANTRIMED {default = 2}. For windowmovement = 'bands', the filter is applied to a window of win(1) rows down each column followed by applying across a window of win(2) columns to each row. For windowmovement = 'rectangular', the filter is applied the windows of size win(1) by win(2) directly (uses more pixels in each filter than 'bands'). | |||
===See Also=== | ===See Also=== | ||
[[spatial_filter]], [[ | [[maxautofactors]], [[spatial_filter]], [[windowfilter]], [[madc]] | ||
Latest revision as of 10:49, 5 December 2019
Purpose
Image or matrix (N-way) filtering (spatial filtering with a moving window)
Synopsis
- xf = box_filter(x,win,options)
Description
BOX_FILTER is a spatial filter that filters and despises based on a moving window (or box). It tends to be slow compared to filter methods that can be vectorized.
Inputs
- x = MxNxP data class 'double' or 'dataset'. If 'dataset' it must x.type=='image' (see BUILDIMAGE). If 'double' it must be MxNxP (P can = 1 or N-way arrays).
- 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].
Outputs
- xf = Filtered image class 'dataset' or matrix (or N-way array) class 'double.'
Options
options = a structure array with the following fields:
- algorithm: [ {'mean'} | 'median' | 'max' | 'min' | 'despike' | 'meantrimmed' | 'mediantrimmed' ] governs filter method.
- window movement: [ {'bands'} | 'rectangle' ], 'bands' moves vertically then horizontally over a single window across the image or slab, and 'rectangle' moves a rectangular window across the image or slab.
despike parameters
- tol: { [] } if empty then despike uses the std(median(x)) within each window to define the tolerance within the window. If tol>0 scalar, then tol defines the tolerance for all the windows. If tol<=0, then tol is estimated by the mean absolute deviation of madc(x.data(:)) and is the tolerance used for all the windows (see MADC).
- dsthreshold: [ {2} ] Threshold used for the 'despike' algorithm. In contrast to replacing all values with the meidan (e.g., for options.algorithm = 'median'), 'despike' replaces only values outside |x-median(x)|>options.dsthreshold*options.tol with the median. (see options.tol)
- trbflag: [ {'middle'} | 'bottom' | 'top' ] top-or-bottom flag. For trbflag = 'middle' the filter replaces values outside |x-median(x)|>dsthreshold*options.tol) with the median. For trbflag = 'bottom' the filter replaces values outside (x-median(x))>dsthreshold*options.tol with the median. For trbflag = 'top' the filter replaces values outside (median(x)-x)>dsthreshold*soptions.tol with the median.
ntrim parameters
- ntrim: when algorithm = 'meantrimmed' or 'mediantrimmed', (ntrim) is the input (n) to the functions MEANTRIMMED or MEDIANTRIMED {default = 2}. For windowmovement = 'bands', the filter is applied to a window of win(1) rows down each column followed by applying across a window of win(2) columns to each row. For windowmovement = 'rectangular', the filter is applied the windows of size win(1) by win(2) directly (uses more pixels in each filter than 'bands').