medpy.filter.noise.separable_convolution

medpy.filter.noise.separable_convolution(input, weights, output=None, mode='reflect', cval=0.0, origin=0)

Calculate a n-dimensional convolution of a separable kernel to a n-dimensional input.

Achieved by calling convolution1d along the first axis, obtaining an intermediate image, on which the next convolution1d along the second axis is called and so on.

Parameters:

inputarray_like

Array of which to estimate the noise.

weightsndarray

One-dimensional sequence of numbers.

outputarray, optional

The output parameter passes an array in which to store the filter output.

mode{‘reflect’, ‘constant’, ‘nearest’, ‘mirror’, ‘wrap’}, optional

The mode parameter determines how the array borders are handled, where cval is the value when mode is equal to ‘constant’. Default is ‘reflect’

cvalscalar, optional

Value to fill past edges of input if mode is ‘constant’. Default is 0.0

originscalar, optional

The origin parameter controls the placement of the filter. Default 0.0.

Returns:

outputndarray

Input image convolved with the supplied kernel.