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

Module: novice

warp_coords skimage.transform.warp_coords(coord_map, shape, dtype=) [source] Build the source coordinates for the output of a 2-D image warp. Parameters: coord_map : callable like GeometricTransform.inverse Return input coordinates for given output coordinates. Coordinates are in the shape (P, 2), where P is the number of coordinates and each element is a (row, col) pair. shape : tuple Shape of output image (rows, cols[, bands]). dtype : np.dtype or string dtype for return value (sane

plot_matches skimage.feature.plot_matches(ax, image1, image2, keypoints1, keypoints2, matches, keypoints_color='k', matches_color=None, only_matches=False) [source] Plot matched features. Parameters: ax : matplotlib.axes.Axes Matches and image are drawn in this ax. image1 : (N, M [, 3]) array First grayscale or color image. image2 : (N, M [, 3]) array Second grayscale or color image. keypoints1 : (K1, 2) array First keypoint coordinates as (row, col). keypoints2 : (K2, 2) array Sec