Code Directory
 Visual Basic & VB.NET
New Code
Rapid PHP 2018 15.5
Online Course Booking Script 1.3.3
Database Workbench Pro 5.6.4
Job Portal Script 1.3.2
The C# PDF Library 5.2
PostgreSQL Data Access Components 6.0
Virtual Data Access Components 11.0
HTMLPad 2018 15.5
WeBuilder 2018 15.5
SentiMask SDK Trial 2.0.0
Track Order For Magento 2 1.0.0
Calendar 365 For Dynamics CRM 4.0
Scimbo 1.2
Odoo Furnito Theme 1.0
Top Code
PostgreSQL Data Access Components 4.4
Database Workbench Pro 5.6.4
Availability Booking Calendar PHP 1.0
Online Course Booking Script 3.04
ATN Site Builder 3.0
ATN Resume Finder 2.0
PHP Review Script 1.0
ICPennyBid Penny Auction Script 4.0
Invoice Manager by PHPJabbers 3.0
The C# PDF Library 1.0
A beginner's guide to threading in C#
How to Read a text file in ASP .NET ?
Overloading Strings in C#
Top Rated
VisualNEO Web 2018.12.15
Azizi search engine script PHP 4.1.10
Paste phpSoftPro 1.4.1
Extreme Injector 3.7
Deals and Discounts Website Script 1.0.2
ADO.NET Provider for ExactTarget 1.0
Solid File System OS edition 5.1
Classified Ad Lister 1.0
Aglowsoft SQL Query Tools 8.2
Invoice Manager by PHPJabbers 3.0
ICPennyBid Penny Auction Script 4.0
PHP Review Script 1.0
ATN Resume Finder 2.0
ATN Site Builder 3.0
Availability Booking Calendar PHP 1.0
Unsupervised Wiener-Hunt deconvolution 1.0
File ID: 83903

Unsupervised Wiener-Hunt deconvolution 1.0
Download Unsupervised Wiener-Hunt deconvolution 1.0http://www.mathworks.comReport Error Link
License: Shareware
File Size: 10.0 KB
Downloads: 12
Submit Rating:
Unsupervised Wiener-Hunt deconvolution 1.0 Description
Description: udeconv - Unsupervised Wiener-Hunt deconvolution

[xEap, gnChain, gxChain] = udeconv(data, ir, reg, criterion, burnin, maxIter)

return the deconvolution of 'data' by 'ir' with the 'reg' regularization operator. The algorithm is a stochastic iterative process (Gibbs sampler) that allow automatic tuning of regularization parameter, see reference below. There is no specific constraints on the number of dimension.

The call [xEap, gnChain, gxChain, xStd] = udeconv(...) allow to compute the diagonal of the covariance matrix around xEap with the cost of an fft at each iteration.

If you use this work, please add a citation of the reference below.

Compatible with octave.


data -- the data

ir -- the impulsionnal response

reg -- the regularisation operator (a laplacian for example)

criterion -- if the difference between two successive estimate is less than this value, stop the algorithm.

burnin -- number of iteration to remove at the beginning of the chain to compute the mean of the image (typicaly 30).

maxIter -- maximum number of iteration (typicaly 200).


xEap -- the estimated result

xStd -- is the standart deviation around the estimate

gnChain, gxChain -- the MCMC chain of the regularisation parameters. See reference below.


[xEap gnChain, gxChain] = udeconv(data, ir, hpFilter, criterion, burnin, maxIter)

[xEap gnChain, gxChain, xStd] = udeconv(...)


FrandoTzois Orieux, Jean-FrandoTzois 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) ://

License: Shareware

Related: beginning, Remove, estimate, Chain, Image, outputs, maximum, typicaly, successive

O/S:BSD, Linux, Solaris, Mac OS X

File Size: 10.0 KB

Downloads: 12

More Similar Code

MISO_FIRWIENER(N, X, Y) computes the optimal FIR Wiener filter of order N, given any number of (stationary) random input signals as the columns of matrix X, and one output signal in column vector Y.

Requires BLOCK_LEVISON.M, also on the file exchange.

By Keenan Pepper; uploaded with permission.

This is the iterative 2D Minimum Entropy Deconvolution implemented according to an iterative method in the original paper:

R.A. Wiggins, Minimum Entropy Deconvolution, Geoexploration, vol. 16, Elsevier Scientific Publishing, Amsterdam,...

SMOOTHN provides a fast, unsupervised and robust discretized spline smoother for data of any dimension.

SMOOTHN(Y) automatically smoothes the uniformly-sampled array Y. Y can be any N-D noisy array (time series, images, 3D data,...).

The tvreg package applies total variation (TV) regularization to perform image denoising, deconvolution, and inpainting. Three different noise models are supported: Gaussian (L2), Laplace (L1), and Poisson. The implementation solves the general TV...

The High Dimensional Data Clustering (HDDC) toolbox contains an efficient unsupervised classifiers for high-dimensional data. This classifier is based on Gaussian models adapted for high-dimensional data.

Reference: C. Bouveyron, S....

This toolbox provides signal/image/3D processing based on Bregman Iterations
This toolbox provides functions mainly to solve sparse algorithms (denoising, deconvolution) for signal processing, image processing and 3D datacube processing. It...

WebVizTools is a set of scripts to help visualize larger web sites and hunt down orphaned files.

The "Schnitzelhunt" is a mobile educational ralley (scavenger hunt) for cellphones. Schnitzelhunt can be used just for fun, but it was conceptualized as a mobile learning tool to support lectures in tradtitional education.

Slickers is a multi player treasure hunt game. CLI interface via NCURSES will be supported, as of immediate goal. Intention is to provide endless multiplayer game play of treasure hunt!

ConsUMA realizes a 'Constraint-Based Unsupervised Morphological Analysis' of natural language. It is designed for (relatively) easy extension and integration into larger Natural Language Processing-, Information Retrieval- or similar...

User Review for Unsupervised Wiener-Hunt deconvolution
- required fields

Please enter text on the image