medianfilter
: Median filter¶
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silx.math.medianfilter.
medfilt
(data, kernel_size=3, bool conditional=False, mode='nearest')¶ Function computing the median filter of the given input. Behavior at boundaries: the algorithm is reducing the size of the window/kernel for pixels at boundaries (there is no mirroring).
Parameters: - data (numpy.ndarray) – the array for which we want to apply the median filter. Should be 1d or 2d.
- kernel_size (For 1D should be an int for 2D should be a tuple or a list of (kernel_height, kernel_width)) – the dimension of the kernel.
- conditional (bool) – True if we want to apply a conditional median filtering.
- mode (str) – the algorithm used to determine how values at borders are determined.
Returns: the array with the median value for each pixel.
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silx.math.medianfilter.
medfilt1d
(data, kernel_size=3, bool conditional=False, mode='nearest')¶ Function computing the median filter of the given input. Behavior at boundaries: the algorithm is reducing the size of the window/kernel for pixels at boundaries (there is no mirroring).
Parameters: - data (numpy.ndarray) – the array for which we want to apply the median filter. Should be 1d.
- kernel_size (int) – the dimension of the kernel.
- conditional (bool) – True if we want to apply a conditional median filtering.
Returns: the array with the median value for each pixel.
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silx.math.medianfilter.
medfilt2d
(image, kernel_size=3, bool conditional=False, mode='nearest')¶ Function computing the median filter of the given input. Behavior at boundaries: the algorithm is reducing the size of the window/kernel for pixels at boundaries (there is no mirroring).
Parameters: - data (numpy.ndarray) – the array for which we want to apply the median filter. Should be 2d.
- kernel_size (For 1D should be an int for 2D should be a tuple or a list of (kernel_height, kernel_width)) – the dimension of the kernel.
- conditional (bool) – True if we want to apply a conditional median filtering.
Returns: the array with the median value for each pixel.