skimage2.restoration.cycle_spin

skimage2.restoration.cycle_spin(x, func, max_shifts, shift_steps=1, num_workers=<DEPRECATED>, func_kw=None, *, workers=None, channel_axis=None)

Cycle spinning (repeatedly apply func to shifted versions of x).

Parameters:

xarray-like

Data for input to func.

funcfunction

A function to apply to circularly shifted versions of x. Should take x as its first argument. Any additional arguments can be supplied via func_kw.

max_shiftsint or tuple

If an integer, shifts in range(0, max_shifts+1) will be used along each axis of x. If a tuple, range(0, max_shifts[i]+1) will be along axis i.

shift_stepsint or tuple, optional

The step size for the shifts applied along axis, i, are:: range((0, max_shifts[i]+1, shift_steps[i])). If an integer is provided, the same step size is used for all axes.

workersint or None, optional

The number of parallel threads to use during cycle spinning. If set to None, the full set of available cores are used.

func_kwdict, optional

Additional keyword arguments to supply to func.

channel_axisint or None, optional

If None, the image is assumed to be a grayscale (single channel) image. Otherwise, this parameter indicates which axis of the array corresponds to channels.

Added in version 0.19: channel_axis was added in 0.19.

Returns:

avg_ynp.ndarray

The output of func(x, **func_kw) averaged over all combinations of the specified axis shifts.

Other Parameters:

num_workersDEPRECATED

Deprecated in favor of workers.

Deprecated since version 0.26.

Notes

Cycle spinning was proposed as a way to approach shift-invariance via performing several circular shifts of a shift-variant transform [1].

For a n-level discrete wavelet transforms, one may wish to perform all shifts up to max_shifts = 2**n - 1. In practice, much of the benefit can often be realized with only a small number of shifts per axis.

For transforms such as the blockwise discrete cosine transform, one may wish to evaluate shifts up to the block size used by the transform.

References

[1]

R.R. Coifman and D.L. Donoho. “Translation-Invariant De-Noising”. Wavelets and Statistics, Lecture Notes in Statistics, vol.103. Springer, New York, 1995, pp.125-150. DOI:10.1007/978-1-4612-2544-7_9

Examples

>>> import _skimage2.data
>>> from _skimage2 import img_as_float
>>> from _skimage2.restoration import denoise_tv_chambolle, cycle_spin
>>> img = img_as_float(_skimage2.data.camera())
>>> sigma = 0.1
>>> img = img + sigma * np.random.standard_normal(img.shape)
>>> denoised = cycle_spin(img, func=denoise_tv_chambolle,
...                       max_shifts=3)