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

threshold_adaptive skimage.filters.threshold_adaptive(image, block_size, method='gaussian', offset=0, mode='reflect', param=None) [source] Applies an adaptive threshold to an array. Also known as local or dynamic thresholding where the threshold value is the weighted mean for the local neighborhood of a pixel subtracted by a constant. Alternatively the threshold can be determined dynamically by a a given function using the ‘generic’ method. Parameters: image : (N, M) ndarray Input image.

RAG class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr) [source] Bases: networkx.classes.graph.Graph The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph Parameters: label_image : array of int An initial segmentation, with each region labeled as a different integer. Every unique value in label_image will correspond to a node in the graph. connectivity : int in {1, ..., label_image.ndim}, optional The connectivity between pixels in label_i

figimage skimage.viewer.utils.figimage(image, scale=1, dpi=None, **kwargs) [source] Return figure and axes with figure tightly surrounding image. Unlike pyplot.figimage, this actually plots onto an axes object, which fills the figure. Plotting the image onto an axes allows for subsequent overlays of axes artists. Parameters: image : array image to plot scale : float If scale is 1, the figure and axes have the same dimension as the image. Smaller values of scale will shrink the figure. d

SimilarityTransform class skimage.transform.SimilarityTransform(matrix=None, scale=None, rotation=None, translation=None) [source] Bases: skimage.transform._geometric.ProjectiveTransform 2D similarity transformation of the form: ..:math: X = a0 * x - b0 * y + a1 = = m * x * cos(rotation) - m * y * sin(rotation) + a1 Y = b0 * x + a0 * y + b1 = = m * x * sin(rotation) + m * y * cos(rotation) + b1 where m is a zoom factor and the homogeneous transformation matrix is: [[a0 b0 a1] [b0 a0 b1

copy_func skimage.filters.copy_func(f, name=None) [source] Create a copy of a function. Parameters: f : function Function to copy. name : str, optional Name of new function.

reset_plugins skimage.io.reset_plugins() [source]

skimage Image Processing SciKit (Toolbox for SciPy) scikit-image (a.k.a. skimage) is a collection of algorithms for image processing and computer vision. The main package of skimage only provides a few utilities for converting between image data types; for most features, you need to import one of the following subpackages:

chelsea skimage.data.chelsea() [source] Chelsea the cat. An example with texture, prominent edges in horizontal and diagonal directions, as well as features of differing scales. Notes No copyright restrictions. CC0 by the photographer (Stefan van der Walt).

page skimage.data.page() [source] Scanned page. This image of printed text is useful for demonstrations requiring uneven background illumination.