dask_image.ndfourier.fourier_gaussian(image, sigma, n=- 1, axis=- 1)[source]

Multi-dimensional Gaussian fourier filter.

The array is multiplied with the fourier transform of a Gaussian kernel.

Parameters
• image (array_like) – The input image.

• sigma (float or sequence) – The sigma of the Gaussian kernel. If a float, sigma is the same for all axes. If a sequence, sigma has to contain one value for each axis.

• n (int, optional) – If n is negative (default), then the image is assumed to be the result of a complex fft. If n is larger than or equal to zero, the image is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.

• axis (int, optional) – The axis of the real transform.

Returns

fourier_gaussian

Return type

Examples

>>> from scipy import ndimage, misc
>>> import numpy.fft
>>> import matplotlib.pyplot as plt
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
>>> plt.gray()  # show the filtered result in grayscale
>>> ascent = misc.ascent()
>>> image = numpy.fft.fft2(ascent)
>>> result = ndimage.fourier_gaussian(image, sigma=4)
>>> result = numpy.fft.ifft2(result)
>>> ax1.imshow(ascent)

dask_image.ndfourier.fourier_shift(image, shift, n=- 1, axis=- 1)[source]

Multi-dimensional fourier shift filter.

The array is multiplied with the fourier transform of a shift operation.

Parameters
• image (array_like) – The input image.

• shift (float or sequence) – The size of the box used for filtering. If a float, shift is the same for all axes. If a sequence, shift has to contain one value for each axis.

• n (int, optional) – If n is negative (default), then the image is assumed to be the result of a complex fft. If n is larger than or equal to zero, the image is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.

• axis (int, optional) – The axis of the real transform.

Returns

fourier_shift

Return type

Examples

>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> import numpy.fft
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
>>> plt.gray()  # show the filtered result in grayscale
>>> ascent = misc.ascent()
>>> image = numpy.fft.fft2(ascent)
>>> result = ndimage.fourier_shift(image, shift=200)
>>> result = numpy.fft.ifft2(result)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result.real)  # the imaginary part is an artifact
>>> plt.show()

dask_image.ndfourier.fourier_uniform(image, size, n=- 1, axis=- 1)[source]

Multi-dimensional uniform fourier filter.

The array is multiplied with the fourier transform of a box of given size.

Parameters
• image (array_like) – The input image.

• size (float or sequence) – The size of the box used for filtering. If a float, size is the same for all axes. If a sequence, size has to contain one value for each axis.

• n (int, optional) – If n is negative (default), then the image is assumed to be the result of a complex fft. If n is larger than or equal to zero, the image is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.

• axis (int, optional) – The axis of the real transform.

Returns

fourier_uniform – The filtered image. If output is given as a parameter, None is returned.

Return type

Examples

>>> from scipy import ndimage, misc
>>> import numpy.fft
>>> import matplotlib.pyplot as plt
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
>>> plt.gray()  # show the filtered result in grayscale
>>> ascent = misc.ascent()
>>> image = numpy.fft.fft2(ascent)
>>> result = ndimage.fourier_uniform(image, size=20)
>>> result = numpy.fft.ifft2(result)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result.real)  # the imaginary part is an artifact
>>> plt.show()