文件列表:

K-SVD dictionary training algorithms\Contents.m (1068, 2009-08-03)
K-SVD dictionary training algorithms\faq.txt (4348, 2009-10-18)
K-SVD dictionary training algorithms\images\barbara.png (185727, 2009-07-27)
K-SVD dictionary training algorithms\images\boat.png (177762, 2009-07-27)
K-SVD dictionary training algorithms\images\house.png (34985, 2009-07-27)
K-SVD dictionary training algorithms\images\lena.png (151199, 2009-07-27)
K-SVD dictionary training algorithms\images\peppers.png (233120, 2009-07-27)
K-SVD dictionary training algorithms\images\Thumbs.db (24064, 2010-01-25)
K-SVD dictionary training algorithms\KSVD-OMP-v2.pdf (171887, 2010-01-25)
K-SVD dictionary training algorithms\ksvd.m (19222, 2009-10-12)
K-SVD dictionary training algorithms\ksvdbox13.zip (837494, 2010-01-25)
K-SVD dictionary training algorithms\ksvddemo.m (1736, 2009-07-27)
K-SVD dictionary training algorithms\ksvddenoise.m (10765, 2010-01-25)
K-SVD dictionary training algorithms\ksvddenoisedemo.m (2419, 2010-10-26)
K-SVD dictionary training algorithms\ksvdver.m (13711, 2009-10-18)
K-SVD dictionary training algorithms\odct2dict.m (1057, 2009-07-27)
K-SVD dictionary training algorithms\odct3dict.m (1149, 2009-07-27)
K-SVD dictionary training algorithms\odctdict.m (452, 2009-07-27)
K-SVD dictionary training algorithms\odctndict.m (1459, 2009-07-27)
K-SVD dictionary training algorithms\ompdenoise.m (10423, 2009-10-03)
K-SVD dictionary training algorithms\ompdenoise1.m (4084, 2009-09-07)
K-SVD dictionary training algorithms\ompdenoise2.m (4059, 2009-09-07)
K-SVD dictionary training algorithms\ompdenoise3.m (4229, 2009-09-07)
K-SVD dictionary training algorithms\private\addtocols.c (1915, 2009-07-27)
K-SVD dictionary training algorithms\private\addtocols.m (303, 2009-07-27)
K-SVD dictionary training algorithms\private\addtocols.mexw32 (6656, 2010-01-25)
K-SVD dictionary training algorithms\private\add_dc.m (830, 2009-07-27)
K-SVD dictionary training algorithms\private\col2imstep.c (3924, 2009-08-31)
K-SVD dictionary training algorithms\private\col2imstep.m (1001, 2009-08-31)
K-SVD dictionary training algorithms\private\col2imstep.mexw32 (7680, 2010-01-25)
K-SVD dictionary training algorithms\private\collincomb.c (4255, 2009-07-27)
K-SVD dictionary training algorithms\private\collincomb.m (832, 2009-07-27)
K-SVD dictionary training algorithms\private\collincomb.mexw32 (7680, 2010-01-25)
K-SVD dictionary training algorithms\private\countcover.m (1178, 2009-09-01)
K-SVD dictionary training algorithms\private\dictdist.m (1839, 2009-07-27)
K-SVD dictionary training algorithms\private\im2colstep.c (3388, 2009-10-18)
K-SVD dictionary training algorithms\private\im2colstep.m (1341, 2009-09-01)
K-SVD dictionary training algorithms\private\im2colstep.mexw32 (7168, 2010-01-25)
K-SVD dictionary training algorithms\private\imnormalize.m (477, 2009-07-27)
K-SVD dictionary training algorithms\private\iswhole.m (470, 2009-07-27)
... ...

KSVDBox v13 README October 18, 2009 KSVDBox installation: --------------------- 1. Make sure OMPBox v10 is installed prior to installing this package. 2. Unpack the contents of the compressed file to a new directory, named e.g. "ksvdbox". 3. If you have not done so before, configure Matlab's MEX compiler by entering >> mex -setup prior to using MAKE. For optimal performance, it is recommended that you select a compiler that performs optimizations. For instance, in Windows, MS Visual Studio is preferred to Lcc. 4. Within Matlab, navigate to the KSVDBox directory, and then to the "private" directory within it, and enter MAKE to run the compilation script. 5. Add the KSVDBox package directory to the Matlab path (you can use the ADDPATH command for this). Do not add the private directory to the path. KSVDBox quick start: -------------------- 1. Enter "ksvddemo" and "ksvddenoisedemo" at the Matlab command prompt to run some demonstrations of the package. 2. For a complete list of functions in the package, enter >> help ksvdbox This assumes the package was installed to a directory named "ksvdbox". If not, replace ksvdbox in the above with the (unqualified) name of the KSVDBox installation directory. Also see faq.txt for some frequently asked questions about the package.

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