References [R303] François Orieux, Jean-François Giovannelli, and Thomas Rodet, “Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution”, J. Opt. Soc. Am. A 27, 1593-1607 (2010) http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593 [R304] Richardson, William Hadley, “Bayesian-Based Iterative Method of Image Restoration”. JOSA 62 (1): 55–59. doi:10.1364/JOSA.62.000055, 1972 [R305] B. R. Hunt “A matrix theory proof of the disc

enhance_contrast skimage.filters.rank.enhance_contrast(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Enhance contrast of an image. This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtyp

random_walker skimage.segmentation.random_walker(data, labels, beta=130, mode='bf', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None) [source] Random walker algorithm for segmentation from markers. Random walker algorithm is implemented for gray-level or multichannel images. Parameters: data : array_like Image to be segmented in phases. Gray-level data can be two- or three-dimensional; multichannel data can be three- or four- dimensional (multichannel=True) wi

Module: exposure skimage.exposure.adjust_gamma(image[, ...]) Performs Gamma Correction on the input image. skimage.exposure.adjust_log(image[, gain, inv]) Performs Logarithmic correction on the input image. skimage.exposure.adjust_sigmoid(image[, ...]) Performs Sigmoid Correction on the input image. skimage.exposure.cumulative_distribution(image) Return cumulative distribution function (cdf) for the given image. skimage.exposure.equalize_adapthist(image, ...) Contrast Limited Adaptive Hist

unwrap_phase skimage.restoration.unwrap_phase(image, wrap_around=False, seed=None) [source] Recover the original from a wrapped phase image. From an image wrapped to lie in the interval [-pi, pi), recover the original, unwrapped image. Parameters: image : 1D, 2D or 3D ndarray of floats, optionally a masked array The values should be in the range [-pi, pi). If a masked array is provided, the masked entries will not be changed, and their values will not be used to guide the unwrapping of nei

load_surf skimage.io.load_surf(f) [source] Read SIFT or SURF features from a file. Parameters: f : string or open file Input file generated by the feature detectors from http://people.cs.ubc.ca/~lowe/keypoints/ or http://www.vision.ee.ethz.ch/~surf/ Returns: data : record array with fields row: int row position of feature column: int column position of feature scale: float feature scale orientation: float feature orientation data: array feature values

LineModelND class skimage.measure.LineModelND [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for N-dimensional lines. Lines are defined by a point (origin) and a unit vector (direction) according to the following vector equation: X = origin + lambda * direction Attributes params (tuple) Line model parameters in the following order origin, direction. __init__() [source] estimate(data) [source] Estimate line model from data. Parameters: data : (N, dim) arra

find_available_plugins skimage.io.find_available_plugins(loaded=False) [source] List available plugins. Parameters: loaded : bool If True, show only those plugins currently loaded. By default, all plugins are shown. Returns: p : dict Dictionary with plugin names as keys and exposed functions as values.

Button class skimage.viewer.widgets.Button(name, callback) [source] Bases: skimage.viewer.widgets.core.BaseWidget Button which calls callback upon click. Parameters: name : str Name of button. callback : callable f() Function to call when button is clicked. __init__(name, callback) [source]

img_as_int skimage.img_as_int(image, force_copy=False) [source] Convert an image to 16-bit signed integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of uint16 Output image. Notes The values are scaled between -32768 and 32767. If the input data-type is positive-only (e.g., uint8), then the output image will still only have positive values.