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 discrete convolution theorem”, IEEE Trans. on Audio and Electroacoustics, vol. au-19, no. 4, pp. 285-288, dec. 1971 |
skimage.restoration.denoise_bilateral(image) | Denoise image using bilateral filter. |
skimage.restoration.denoise_nl_means(image) | Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images. |
skimage.restoration.denoise_tv_bregman(...) | Perform total-variation denoising using split-Bregman optimization. |
skimage.restoration.denoise_tv_chambolle(im) | Perform total-variation denoising on n-dimensional images. |
skimage.restoration.nl_means_denoising(...) |
Deprecated function. Use skimage.restoration.denoise_nl_means instead. |
skimage.restoration.richardson_lucy(image, psf) | Richardson-Lucy deconvolution. |
skimage.restoration.unsupervised_wiener(...) | Unsupervised Wiener-Hunt deconvolution. |
skimage.restoration.unwrap_phase(image[, ...]) | Recover the original from a wrapped phase image. |
skimage.restoration.wiener(image, psf, balance) | Wiener-Hunt deconvolution |
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