convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0)

Convolve :None:None:`in1` and in2 with output size determined by :None:None:`mode`, and boundary conditions determined by boundary and :None:None:`fillvalue`.

Parameters

in1 : array_like

First input.

in2 : array_like

Second input. Should have the same number of dimensions as :None:None:`in1`.

mode : str {'full', 'valid', 'same'}, optional

A string indicating the size of the output:

full

The output is the full discrete linear convolution of the inputs. (Default)

valid

The output consists only of those elements that do not rely on the zero-padding. In 'valid' mode, either :None:None:`in1` or in2 must be at least as large as the other in every dimension.

same

The output is the same size as :None:None:`in1`, centered with respect to the 'full' output.

boundary : str {'fill', 'wrap', 'symm'}, optional

A flag indicating how to handle boundaries:

fill

pad input arrays with fillvalue. (default)

wrap

circular boundary conditions.

symm

symmetrical boundary conditions.

fillvalue : scalar, optional

Value to fill pad input arrays with. Default is 0.

Returns

out : ndarray

A 2-dimensional array containing a subset of the discrete linear convolution of :None:None:`in1` with in2 .

Convolve two 2-dimensional arrays.

Examples

Compute the gradient of an image by 2D convolution with a complex Scharr operator. (Horizontal operator is real, vertical is imaginary.) Use symmetric boundary condition to avoid creating edges at the image boundaries.

>>> from scipy import signal
... from scipy import misc
... ascent = misc.ascent()
... scharr = np.array([[ -3-3j, 0-10j,  +3 -3j],
...                    [-10+0j, 0+ 0j, +10 +0j],
...                    [ -3+3j, 0+10j,  +3 +3j]]) # Gx + j*Gy
... grad = signal.convolve2d(ascent, scharr, boundary='symm', mode='same')

>>> import matplotlib.pyplot as plt
... fig, (ax_orig, ax_mag, ax_ang) = plt.subplots(3, 1, figsize=(6, 15))
... ax_orig.imshow(ascent, cmap='gray')
... ax_orig.set_title('Original')
... ax_orig.set_axis_off()
... ax_mag.imshow(np.absolute(grad), cmap='gray')
... ax_mag.set_title('Gradient magnitude')
... ax_mag.set_axis_off()
... ax_ang.imshow(np.angle(grad), cmap='hsv') # hsv is cyclic, like angles
... ax_ang.set_title('Gradient orientation')
... ax_ang.set_axis_off()
... fig.show()

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.restoration.deconvolution.unsupervised_wiener skimage.restoration.deconvolution.wiener scipy.signal._signaltools.fftconvolve skimage.restoration.deconvolution.richardson_lucy scipy.signal._signaltools.convolve2d

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