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tvreg: Variational Imaging Methods for Denoising, Deconvolution, Inpainting, and Segmentation

4 years 9 months ago
tvreg: Variational Imaging Methods for Denoising, Deconvolution, Inpainting, and Segmentation
TV-based image restoration with options for deconvolution, inpainting, and different noise models. Chan-Vese segmentation also included. Usable from C, C++, or MATLAB.
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tvreg.zip618.68 KB
Added 01 Dec 2009
Updated 05 Sep 2011
OS All OS
Language C
Attachments 1 file(s)

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 restoration problem

min_u TV(u) + int lambda F(K*u,f) dx

to perform denoising, deconvolution, and inpainting as special cases.  It is efficiently solved using the recent split Bregman method.  Also included is an efficient implementation of Chan-Vese two-phase segmentation.

Please see the included documentation file tvreg.pdf for details.

Get Started Quickly

  1. Install the FFTW3 library. Windows users can download precompiled DLL files from http://www.fftw.org/install/windows.html.
  2. Compile the programs with GCC using make -f makefile.gcc or Microsoft Visual C++ using nmake -f makefile.vc. See section 7 of the documentation for help.
  3. Try the demos
    tvdenoise_demo   Total variation denoising demo
    tvdeconv_demoTotal variation deconvolution demo
    tvinpaint_demoTotal variation inpainting demo
    chanvese_demoChan-Vese segmentation demo

Get Started Quickly in MATLAB

Compiling is not required to use tvreg in Matlab. Try the demos

tvdenoise_demo   Total variation denoising demo
tvdeconv_demoTotal variation deconvolution demo
tvinpaint_demoTotal variation inpainting demo
chanvese_demoChan-Vese segmentation demo

For improved performance, run the included script complex_mex.m to compile the main computation routines as MEX functions.  This requires that FFTW3 is installed, please see section 7.3 of the documentation.

This material is based upon work supported by the National Science Foundation under Award No. DMS-1004694. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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