SR
所属分类：图形图像处理
开发工具：matlab
文件大小：8423KB
下载次数：552
上传日期：2011-04-24 16:11:40
上 传 者：
Tom88
说明： 利用稀疏矩阵思想进行图像的超分辨率重建，效果较好。
(Thinking of using sparse matrix for image super-resolution reconstruction, the effect is better.)
文件列表:
ScSR\backprojection.m (460, 2011-01-28)
ScSR\compute_rmse.m (293, 2011-01-12)
ScSR\Data\Testing\gnd.bmp (196662, 2011-01-10)
ScSR\Data\Testing\input.bmp (49206, 2011-01-18)
ScSR\Data\Testing\result.bmp (196662, 2011-01-28)
ScSR\Data\Training\t1.bmp (104246, 2007-10-14)
ScSR\Data\Training\t11.bmp (118614, 2007-10-14)
ScSR\Data\Training\t12.bmp (40990, 2007-10-14)
ScSR\Data\Training\t13.bmp (101010, 2007-10-14)
ScSR\Data\Training\t14.bmp (76990, 2007-10-14)
ScSR\Data\Training\t16.bmp (83894, 2007-10-14)
ScSR\Data\Training\t17.bmp (69786, 2007-10-14)
ScSR\Data\Training\t18.bmp (55782, 2007-10-14)
ScSR\Data\Training\t19.bmp (96174, 2007-10-14)
ScSR\Data\Training\t2.bmp (92418, 2007-10-14)
ScSR\Data\Training\t20.bmp (18462, 2007-10-14)
ScSR\Data\Training\t21.bmp (41346, 2007-10-14)
ScSR\Data\Training\t22.bmp (59718, 2007-10-14)
ScSR\Data\Training\t23.bmp (46902, 2007-10-14)
ScSR\Data\Training\t24.bmp (37290, 2007-10-14)
ScSR\Data\Training\t25.bmp (125190, 2007-10-14)
ScSR\Data\Training\t26.bmp (58742, 2007-10-14)
ScSR\Data\Training\t27.bmp (126030, 2007-10-14)
ScSR\Data\Training\t28.bmp (92550, 2007-10-14)
ScSR\Data\Training\t3.bmp (89334, 2007-10-14)
ScSR\Data\Training\t30.bmp (61662, 2007-10-14)
ScSR\Data\Training\t31.bmp (101670, 2007-11-09)
ScSR\Data\Training\t32.bmp (86646, 2007-11-09)
ScSR\Data\Training\t34.bmp (66362, 2007-11-09)
ScSR\Data\Training\t35.bmp (125306, 2007-11-09)
ScSR\Data\Training\t36.bmp (114102, 2007-11-23)
ScSR\Data\Training\t37.bmp (312390, 2007-11-22)
ScSR\Data\Training\t38.bmp (200054, 2007-11-22)
ScSR\Data\Training\t39.bmp (197350, 2007-11-22)
ScSR\Data\Training\t4.bmp (128646, 2007-10-14)
ScSR\Data\Training\t40.bmp (200082, 2007-11-22)
ScSR\Data\Training\t42.bmp (202814, 2007-11-23)
ScSR\Data\Training\t43.bmp (157134, 2007-11-23)
ScSR\Data\Training\t44.bmp (115722, 2007-11-23)
ScSR\Data\Training\t46.bmp (355146, 2007-11-22)
... ...
*****************************************************************
* Demo Codes For Image Super-resolution via Sparse Representation
*****************************************************************
Reference
J. Yang et al. Image super-resolution as sparse representation of raw image patches. CVPR 2008.
J. Yang et al. Image super-resolution via sparse representation. IEEE Transactions on Image Processing, Vol 19, Issue 11, pp2861-2873, 2010
For any problems, send email to jyang29@uiuc.edu
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Demo_SR.m: demo code for image super-resolution via sparse recovery
1. The demo code is for upscaling factor of 2. For larger magnification factors, run the function "ScSR.m" multiple times. Note the code is a little different from what presented in the paper.
2. Two pre-trained dictionaries are provided in directory "Dictionary". The dictionaries are for zoom factor of 2. You can train your own dictionary based on function "Demo_Dictionary_Training.m" talked below.
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Demo_Dictionary_Training.m: demo code for training the dictionary
1. If you want to train your own dictionary, replace the training images in subfolder "Data/Training" by yours.
2. You need to inspect the statistics of your sampled patches to prune those smooth patches.
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