jax.scipy.signal.convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0, precision=None)[source]ΒΆ

Convolve two 2-dimensional arrays.

LAX-backend implementation of convolve2d(). Original docstring below.

Convolve in1 and in2 with output size determined by mode, and boundary conditions determined by boundary and fillvalue.

  • in1 (array_like) – First input.

  • in2 (array_like) – Second input. Should have the same number of dimensions as in1.

  • mode (str {'full', 'valid', 'same'}, optional) – A string indicating the size of the output:

  • boundary (str {'fill', 'wrap', 'symm'}, optional) – A flag indicating how to handle boundaries:

  • fillvalue (scalar, optional) – Value to fill pad input arrays with. Default is 0.


out – A 2-dimensional array containing a subset of the discrete linear convolution of in1 with in2.

Return type



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()