SparseLab21-Core

所属分类:matlab编程
开发工具:matlab
文件大小:31992KB
下载次数:959
上传日期:2009-05-06 11:34:37
上 传 者zhaohaisheng213
说明:  一种基于压缩感知技术的图像重建程序,可以提高图像重建质量和速度
(Compressed sensing technology based on the image reconstruction process, can improve the quality and speed of image reconstruction)

文件列表:
SparseLab2.1-Core (0, 2007-08-09)
SparseLab2.1-Core\.DS_Store (6148, 2007-08-24)
__MACOSX (0, 2007-08-24)
__MACOSX\SparseLab2.1-Core (0, 2007-08-24)
__MACOSX\SparseLab2.1-Core\._.DS_Store (82, 2007-08-24)
SparseLab2.1-Core\CompSense (0, 2006-12-26)
SparseLab2.1-Core\CompSense\CompSense_Elad_IEEETSP.pdf (243852, 2006-12-26)
SparseLab2.1-Core\CompSense\CompSense_Fig2.m (551, 2007-01-02)
SparseLab2.1-Core\CompSense\CompSense_Fig3.m (3914, 2007-01-02)
SparseLab2.1-Core\CompSense\CompSense_Fig4.m (4056, 2007-01-02)
SparseLab2.1-Core\CompSense\CompSense_Fig5.m (4056, 2007-01-02)
SparseLab2.1-Core\CompSense\CompSense_Fig6.m (6925, 2007-01-02)
SparseLab2.1-Core\CompSense\CompSense_Fig7.m (6791, 2007-01-02)
SparseLab2.1-Core\CompSense\mulmd.m (168, 2007-03-24)
SparseLab2.1-Core\Contents.m (2026, 2006-07-30)
SparseLab2.1-Core\Documentation (0, 2007-05-25)
SparseLab2.1-Core\Documentation\.DS_Store (12292, 2007-08-24)
__MACOSX\SparseLab2.1-Core\Documentation (0, 2007-08-24)
__MACOSX\SparseLab2.1-Core\Documentation\._.DS_Store (82, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab (0, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\.DS_Store (6148, 2007-08-24)
__MACOSX\SparseLab2.1-Core\Documentation\AboutSparseLab (0, 2007-08-24)
__MACOSX\SparseLab2.1-Core\Documentation\AboutSparseLab\._.DS_Store (82, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.aux (2917, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.dvi (55296, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.log (7557, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.pdf (126295, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.ps (284157, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.tex (44145, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\AboutSparseLab.toc (2192, 2007-08-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\Contents.m (679, 2007-03-24)
SparseLab2.1-Core\Documentation\AboutSparseLab\References.tex (1119, 2006-05-06)
SparseLab2.1-Core\Documentation\AboutSparseLab\SparseMacros.tex (1628, 2007-03-24)
SparseLab2.1-Core\Documentation\ADDINGNEWFEATURES.m (245, 2006-07-30)
SparseLab2.1-Core\Documentation\BUGREPORT.m (245, 2006-07-30)
SparseLab2.1-Core\Documentation\Contents.m (1537, 2007-03-24)
SparseLab2.1-Core\Documentation\COPYING.m (245, 2006-07-30)
SparseLab2.1-Core\Documentation\DATASTRUCTURES.m (692, 2006-07-30)
SparseLab2.1-Core\Documentation\FEEDBACK.m (657, 2006-07-30)
SparseLab2.1-Core\Documentation\GETTINGSTARTED.m (825, 2006-07-30)
... ...

This set of Matlab (7.0) functions contain the code for generating the figures of the following paper: "Bayesian Compressive Sensing" Shihao Ji, Ya Xue, and Lawrence Carin (Preprint, 2007) The code is still in development stage. If you have any comments or bug reports, you are welcome to contact Shihao Ji at shji@ece.duke.edu. Shihao Ji Duke University, July 19, 2007 *************************************************************************** Distribution and use of this code is subject to the following agreement: This Program is provided by Duke University and the authors as a service to the research community. It is provided without cost or restrictions, except for the User's acknowledgement that the Program is provided on an "As Is" basis and User understands that Duke University and the authors make no express or implied warranty of any kind. Duke University and the authors specifically disclaim any implied warranty or merchantability or fitness for a particular purpose, and make no representations or warranties that the Program will not infringe the intellectual property rights of others. The User agrees to indemnify and hold harmless Duke University and the authors from and against any and all liability arising out of User's use of the Program. ***************************************************************************

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