skimage2.restoration.richardson_lucy#

skimage2.restoration.richardson_lucy(image, psf, num_iter=50, clip=True, filter_epsilon=None)[source]#

Richardson-Lucy deconvolution.

Parameters:
image([P, ]M, N) ndarray

Input degraded image (can be n-dimensional). If you keep the default clip=True parameter, you may want to normalize the image so that its values fall in the [-1, 1] interval to avoid information loss.

psfndarray

The point spread function.

num_iterint, optional

Number of iterations. This parameter plays the role of regularisation.

clipbool, optional

True by default. If true, pixel value of the result above 1 or under -1 are thresholded for skimage pipeline compatibility.

filter_epsilonfloat, optional

Value below which intermediate results become 0 to avoid division by small numbers.

Returns:
im_deconvndarray

The deconvolved image.

References

Examples

>>> from _skimage2 import img_as_float, data, restoration
>>> camera = img_as_float(data.camera())
>>> from scipy.signal import convolve2d
>>> psf = np.ones((5, 5)) / 25
>>> camera = convolve2d(camera, psf, 'same')
>>> rng = np.random.default_rng()
>>> camera += 0.1 * camera.std() * rng.standard_normal(camera.shape)
>>> deconvolved = restoration.richardson_lucy(camera, psf, 5)