TV
所属分类:图形图像处理
开发工具:matlab
文件大小:745KB
下载次数:464
上传日期:2012-07-23 10:56:48
上 传 者:
ptlove
说明: TV算法的MATLAB版,绝对好用,已经经过本人测试过的
(TV algorithm in MATLAB version, absolutely easy to)
文件列表:
TV程序包 (0, 2012-04-05)
TV程序包\tvreg (0, 2012-04-27)
TV程序包\tvreg\blurry.bmp (11970, 2010-12-15)
TV程序包\tvreg\chanvese.c (28818, 2010-12-16)
TV程序包\tvreg\chanvese.h (1191, 2010-12-16)
TV程序包\tvreg\chanvese.m (8243, 2010-12-16)
TV程序包\tvreg\chanvesecli.c (14798, 2010-12-16)
TV程序包\tvreg\chanvesesimpleplot.m (872, 2010-12-16)
TV程序包\tvreg\chanvese_demo.bat (717, 2010-12-16)
TV程序包\tvreg\chanvese_demo.m (977, 2010-12-16)
TV程序包\tvreg\chanvese_demo.sh (739, 2010-12-16)
TV程序包\tvreg\cliio.c (12428, 2010-12-16)
TV程序包\tvreg\cliio.h (1515, 2010-12-16)
TV程序包\tvreg\compareimages.m (1103, 2010-12-16)
TV程序包\tvreg\compile_mex.m (3916, 2010-12-16)
TV程序包\tvreg\conv2padded.m (1380, 2010-12-16)
TV程序包\tvreg\einstein.png (7748, 2009-11-30)
TV程序包\tvreg\imageio.c (62331, 2010-12-16)
TV程序包\tvreg\imageio.h (3366, 2010-12-16)
TV程序包\tvreg\lady.bmp (196662, 2007-10-04)
TV程序包\tvreg\lenna.bmp (66614, 2003-05-05)
TV程序包\tvreg\license.txt (1366, 2010-12-16)
TV程序包\tvreg\lighthouse.bmp (5892, 2010-12-13)
TV程序包\tvreg\makefile.gcc (1837, 2010-12-15)
TV程序包\tvreg\makefile.vc (2199, 2010-12-15)
TV程序包\tvreg\num.h (970, 2010-12-16)
TV程序包\tvreg\signal-D.bmp (2590, 2010-12-15)
TV程序包\tvreg\signal-f.bmp (16900, 2010-12-15)
TV程序包\tvreg\tvdeconv.m (2122, 2010-12-16)
TV程序包\tvreg\tvdeconv_demo.bat (718, 2010-12-16)
TV程序包\tvreg\tvdeconv_demo.m (777, 2012-01-06)
TV程序包\tvreg\tvdeconv_demo.sh (742, 2010-12-16)
TV程序包\tvreg\tvdenoise.m (2025, 2010-12-16)
TV程序包\tvreg\tvdenoise_demo.bat (1350, 2010-12-16)
TV程序包\tvreg\tvdenoise_demo.m (534, 2012-01-06)
TV程序包\tvreg\tvdenoise_demo.sh (1348, 2010-12-16)
TV程序包\tvreg\tvinpaint.m (2181, 2010-12-16)
TV程序包\tvreg\tvinpaint_demo.bat (733, 2010-12-16)
TV程序包\tvreg\tvinpaint_demo.m (636, 2012-01-06)
... ...
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.
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