全变差正则化图像处理(Pascal Getreuer )

tvreg v2: Variational Imaging Methods for Denoising, Deconvolution, Inpainting, and Segmentation Pascal Getreuer, December 2010 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 TV(u) + | lambda F(K*u,f) dx, u / 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. All functions support grayscale, color, and arbitrary multichannel images. Please see the included documentation file tvreg.pdf for details. --------------------- Get Started Quickly --------------------- 1. Install the FFTW library (http://www.fftw.org). 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_demo Total variation deconvolution demo tvinpaint_demo Total variation inpainting demo chanvese_demo Chan-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_demo Total variation deconvolution demo tvinpaint_demo Total variation inpainting demo chanvese_demo Chan-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 FFTW 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.