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ASR_fusion

于 2020-07-13 发布 文件大小:1873KB
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代码说明：

  该文件包含与以下文件相关的代码： Y.Liu和Z.Wang具有自适应稀疏表示的同时图像融合和去噪(This package contains the code which is associated with the following paper: Y. Liu and Z. Wang Simultaneous image fusion and denoising with adaptive sparse representatio)

文件列表：

ASR_fusion\asr_fuse.m, 3879 , 2017-11-21
ASR_fusion\Data\a01_1.tif, 59800 , 2014-09-01
ASR_fusion\Data\a01_2.tif, 60544 , 2014-09-01
ASR_fusion\Data\b01_1.tif, 66346 , 2014-09-01
ASR_fusion\Data\b01_2.tif, 66328 , 2014-09-01
ASR_fusion\Dictionary\D_100000_128_8_0.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_0.mat, 63065 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_1.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_2.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_3.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_4.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_5.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_6.bmp, 10878 , 2014-03-02
ASR_fusion\Dictionary\D_100000_128_8_6.mat, 377390 , 2014-03-02
ASR_fusion\Dictionary\D_100000_256_8_0.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_0.mat, 125987 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_1.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_2.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_3.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_4.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_5.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_6.bmp, 21670 , 2014-03-17
ASR_fusion\Dictionary\D_100000_256_8_6.mat, 754680 , 2014-03-17
ASR_fusion\ksvdbox\Contents.m, 1068 , 2014-09-01
ASR_fusion\ksvdbox\faq.txt, 2725 , 2014-09-01
ASR_fusion\ksvdbox\ksvd.asv, 19553 , 2014-09-01
ASR_fusion\ksvdbox\ksvd.m, 19574 , 2014-09-01
ASR_fusion\ksvdbox\ksvddemo.m, 1757 , 2014-09-01
ASR_fusion\ksvdbox\ksvddenoise.m, 10844 , 2014-09-01
ASR_fusion\ksvdbox\ksvddenoisedemo.m, 2515 , 2014-09-01
ASR_fusion\ksvdbox\ksvdver.m, 12581 , 2014-09-01
ASR_fusion\ksvdbox\odct2dict.m, 1084 , 2014-09-01
ASR_fusion\ksvdbox\odct3dict.m, 1178 , 2014-09-01
ASR_fusion\ksvdbox\odctdict.m, 474 , 2014-09-01
ASR_fusion\ksvdbox\odctndict.m, 1504 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\Contents.m, 559 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\faq.txt, 1870 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\omp.m, 4601 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\omp2.m, 5519 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\ompdemo.m, 2386 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\ompspeedtest.m, 1633 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\ompver.m, 7654 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\.gitignore, 8 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\make.m, 838 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\myblas.c, 5353 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\myblas.h, 6831 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\omp2mex.c, 3540 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\omp2mex.m, 919 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\omp2mex.mexa64, 26906 , 2015-11-07
ASR_fusion\ksvdbox\ompbox\private\omp2mex.mexw32, 16896 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\omp2mex.mexw64, 22528 , 2015-01-31
ASR_fusion\ksvdbox\ompbox\private\ompcore.c, 14351 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\ompcore.h, 3122 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\ompmex.c, 2874 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\ompmex.m, 814 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\ompmex.mexa64, 26905 , 2015-11-07
ASR_fusion\ksvdbox\ompbox\private\ompmex.mexw32, 16384 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\ompmex.mexw64, 22528 , 2015-01-31
ASR_fusion\ksvdbox\ompbox\private\ompprof.c, 4523 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\ompprof.h, 3086 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\omputils.c, 1453 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\omputils.h, 2238 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\private\printf.m, 693 , 2014-09-01
ASR_fusion\ksvdbox\ompbox\readme.txt, 1302 , 2014-09-01
ASR_fusion\ksvdbox\ompdenoise.m, 10481 , 2014-09-01
ASR_fusion\ksvdbox\ompdenoise1.m, 3708 , 2014-09-01
ASR_fusion\ksvdbox\ompdenoise2.m, 5203 , 2014-09-01
ASR_fusion\ksvdbox\ompdenoise3.m, 5700 , 2014-09-01
ASR_fusion\ksvdbox\private\.gitignore, 8 , 2014-09-01
ASR_fusion\ksvdbox\private\addtocols.c, 2000 , 2014-09-01
ASR_fusion\ksvdbox\private\addtocols.m, 316 , 2014-09-01
ASR_fusion\ksvdbox\private\addtocols.mexa64, 8466 , 2015-11-07
ASR_fusion\ksvdbox\private\addtocols.mexw32, 6656 , 2014-09-01
ASR_fusion\ksvdbox\private\addtocols.mexw64, 7680 , 2015-01-31
ASR_fusion\ksvdbox\private\add_dc.m, 830 , 2014-09-01
ASR_fusion\ksvdbox\private\col2imsum.m, 1084 , 2014-09-01
ASR_fusion\ksvdbox\private\collincomb.c, 4420 , 2014-09-01
ASR_fusion\ksvdbox\private\collincomb.m, 859 , 2014-09-01
ASR_fusion\ksvdbox\private\collincomb.mexa64, 12643 , 2015-11-07
ASR_fusion\ksvdbox\private\collincomb.mexw32, 7680 , 2014-09-01
ASR_fusion\ksvdbox\private\collincomb.mexw64, 9728 , 2015-01-31
ASR_fusion\ksvdbox\private\countcover.m, 1163 , 2014-09-01
ASR_fusion\ksvdbox\private\dictdist.m, 1839 , 2014-09-01
ASR_fusion\ksvdbox\private\imnormalize.m, 496 , 2014-09-01
ASR_fusion\ksvdbox\private\iswhole.m, 487 , 2014-09-01
ASR_fusion\ksvdbox\private\make.m, 861 , 2014-09-01
ASR_fusion\ksvdbox\private\mexutils.c, 2219 , 2014-09-01
ASR_fusion\ksvdbox\private\mexutils.h, 3655 , 2014-09-01
ASR_fusion\ksvdbox\private\normcols.m, 346 , 2014-09-01
ASR_fusion\ksvdbox\private\printf.m, 693 , 2014-09-01
ASR_fusion\ksvdbox\private\reggrid.m, 4708 , 2014-09-01
ASR_fusion\ksvdbox\private\remove_dc.m, 1019 , 2014-09-01
ASR_fusion\ksvdbox\private\rowlincomb.c, 3590 , 2014-09-01
ASR_fusion\ksvdbox\private\rowlincomb.m, 861 , 2014-09-01
ASR_fusion\ksvdbox\private\rowlincomb.mexa64, 12643 , 2015-11-07
ASR_fusion\ksvdbox\private\rowlincomb.mexw32, 7168 , 2014-09-01
ASR_fusion\ksvdbox\private\rowlincomb.mexw64, 9728 , 2015-01-31
ASR_fusion\ksvdbox\private\sampgrid.m, 2003 , 2014-09-01
ASR_fusion\ksvdbox\private\secs2hms.m, 671 , 2014-09-01
ASR_fusion\ksvdbox\private\spdiag.m, 769 , 2014-09-01

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  • sparse
    本压缩包包含了图像处理中稀疏表示的相关文献以及相关代码(This archive contains a sparse representation of image processing literature and related code)
    2011-07-01 10:31:26下载
    积分：1
  • Image-processing
    基于MATLAB的系统分析与设计：图像处理(System Analysis and Design: Image processing based on MATLAB)
    2013-01-23 12:12:19下载
    积分：1
  • ImgZoom
    OPENCV做图像变形的经典代码啊，适合比较初级的同学看看(OPENCV do image warping the classic code ah, suitable for elementary students to see more)
    2009-12-02 20:06:13下载
    积分：1
  • gmm
    混合高斯模型使用K（基本为3到5个） 个高斯模型来表征图像中各个像素点的特征,在新一帧图像获得后更新混合高斯模型,用当前图像中的每个像素点与混合高斯模型匹配,如果成功则判定该点为背景点, 否则为前景点。通观整个高斯模型，他主要是有方差和均值两个参数决定，,对均值和方差的学习,采取不同的学习机制,将直接影响到模型的稳定性、精确性和收敛性。由于我们是对运动目标的背景提取建模，因此需要对高斯模型中方差和均值两个参数实时更新。为提高模型的学习能力,改进方法对均值和方差的更新采用不同的学习率 为提高在繁忙的场景下,大而慢的运动目标的检测效果,引入权值均值的概念,建立背景图像并实时更新,然后结合权值、权值均值和背景图像对像素点进行前景和背景的分类。(Gaussian mixture model using K (essentially 3-5) Gaussian model to characterize the features of each pixel in the image, in the image of the new frame for updated Gaussian mixture model, with each pixel in the image with a Gaussian mixture current model matching, if successful, determined that the point of the background points, otherwise the former attraction. Throughout the entire Gaussian model, he mainly has two parameters determine the variance and the mean, the mean and variance of the study, to take a different learning mechanism, will directly affect the stability, accuracy and convergence model. Since we are moving object extraction of the background modeling, so the need for the Gaussian model variance and mean two parameters real-time updates. In order to improve the learning ability of the model, an improved method for updating the mean and variance of different learning rates to improve in the busy scene, large and slow moving object detection results, the introduction of)
    2014-03-25 09:01:12下载
    积分：1
  • Untitled
    说明：  计算BBO晶体一类匹配时随非共线角变化的相位匹配角(Calculating the phase matching angle of BBO crystal with non-collinear angle)
    2021-05-08 17:28:35下载
    积分：1
  • register
    点云的配准实验s-icp算法 实现两个点云数据的对准(A scale stretch method based on ICP for 3D data registration)
    2020-11-12 20:19:44下载
    积分：1
  • IMM-target-tracking
    信息融合滤波算法，基于IMM完成匀速—加速—匀速单运动目标跟踪。(Information fusion filter algorithm based on IMM to complete the uniform and accelerate uniform single moving target tracking.)
    2012-06-25 19:17:54下载
    积分：1
  • SpeckleReducingAnisotropicDiffusion
    说明：  基于各向异性扩散方程的一款经典的ＳＡＲ图像相干斑噪声抑制的算法实现，可以直接用的，效果很好！(Anisotropic diffusion equation based on a classic SAR image speckle noise reduction algorithm can be directly used, very good!)
    2009-07-31 16:36:26下载
    积分：1
  • Keystone-Transfor
    Keystone Transform in ground moving target detection，it s very useful new comer(Keystone Transform in ground moving target detection)
    2020-08-17 21:38:23下载
    积分：1
  • test_batch_mod
    说明：  可完成对MODIS09GA数据的批量格式转换与裁剪(Batch format conversion and clipping of modis09GA data)
    2020-12-09 10:59:23下载
    积分：1
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