Package: edwin
The Bayesian inversion (edwin) package provides algorithm
developed during my scientific research work in numerical computation
for inverse problems (signal, image processing). Feel free to use them
as you want. Any comments and contributions are welcome.The nameedwinis in reference to Edwin T. Jaynes, a great
Bayesian Analysis scientific.AcknowledgementsThe use ofedwinsoftware package should be explicitly
acknowledged in publications in the following form:an acknowledgment statement: “Some of the results in this paper
have been derived using some of theedwinpackage algorithms
From F. Orieux et al. published incitations.at the first reference, a footnote placed in the main body of the
paper referring to theedwinweb site, currentlyhttp://bitbucket.org/forieux/edwinThe citations are mentioned in documentation,Referencessection of
this file and are available in bibtex file.InfoAuthor: François OrieuxContact: orieux at iap dot frProject homepage:http://bitbucket.org/forieux/edwinDownloads page:https://bitbucket.org/forieux/edwin/downloadsContentsimprocessing.pyA module that implement the algorithm described in [2] for
unsupervised myopic image deconvolution. However the myopic part
is not actually available.inversion.pyA module that implement the algorithm described in [1] and use in
[3-4] and other papers. It’s implement an unsupervised general
inverse problem algorithm estimation, based on MCMC algorithm.sampling.pyImplementation of stochastic sampling algorithm, specially [1].optim.pyA module that implement classical optimisation algorithm for use
of other module. They are design for very large system resolution
(dim > 1e6).RequirementsThis package depends on my free otb package (utility functions).Numpy version >= 1.4.1otbversion >= 0.2.1InstallationThepipversion:pip install edwinIf you have notpip, download the archive, decompress it and to
install in your user path, run in a command line:python setup.py install --useror for the system path, run as root:python setup.py installDevelopmentThis package follow the Semantic Versionning conventionhttp://semver.org/. To get the development version you can clone the
mercurial repository available herehttp://bitbucket.org/forieux/edwinThe ongoing development depends on my research activity but is open. I
try to fix bugs.Licenseedwinis free software distributed under the MIT license, see
LICENSE.txtReferencesA bibtex file is provided in the archive.[1]F. Orieux, O. Féron and J.-F. Giovannelli, “Sampling
high-dimensional Gaussian distributions for general linear inverse
problems”, IEEE Signal Processing Letters, 2012[2]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)[3]F. Orieux, E. Sepulveda, V. Loriette, B. Dubertret and
J.-C. Olivo-Marin, “Bayesian Estimation for Optimized Structured
Illumination Microscopy”, IEEE trans. on Image Processing. 2012[4]F. Orieux, J.-F. Giovannelli, T. Rodet, and A. Abergel,
“Estimating hyperparameters and instrument parameters in
regularized inversion Illustration for Herschel/SPIRE map
making”, Astronomy & Astrohpysics, 2013