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沙威 OMP

于 2020-07-04 发布 文件大小:1329KB
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下载积分: 1 下载次数: 11

代码说明:

  压缩感知中经典的匹配追踪算法,稀疏变换包含小波基和离散余弦变换基。(The classical matching pursuit algorithm in compressed sensing includes sparse wavelet transform and discrete cosine transform base.)

文件列表:

沙威_OMP\Compressive_Sensing_沙威.pdf, 208431 , 2010-03-25
沙威_OMP\CS_OMP.m, 2798 , 2009-10-18
沙威_OMP\DCT\DCT变换.jpg, 24558 , 2018-06-29
沙威_OMP\DCT\Demo_CS_OMP.m, 3897 , 2018-06-29
沙威_OMP\DCT\imagesc显示.jpg, 61095 , 2018-06-29
沙威_OMP\DCT\imshow显示.jpg, 32678 , 2018-06-29
沙威_OMP\DCT\lena.bmp, 66614 , 2010-05-11
沙威_OMP\DCT\phi.m, 708 , 2018-06-29
沙威_OMP\DCT\模型.bmp, 196662 , 2018-06-20
沙威_OMP\DWT.m, 1065 , 2009-10-14
沙威_OMP\lena256.bmp, 66614 , 2009-06-08
沙威_OMP\Read Me.txt, 230 , 2010-03-25
沙威_OMP\Wavelet_OMP.m, 2748 , 2018-07-01
沙威_OMP\zhuanhuan.m, 103 , 2018-06-20
沙威_OMP\朵朵姐姐重建图像.png, 17971 , 2018-06-20
沙威_OMP\模型.bmp, 196662 , 2018-06-20
沙威_OMP\模型1025.bmp, 3152954 , 2018-06-20
沙威_OMP\模型535.bmp, 860334 , 2018-06-20
沙威_OMP\结果图\模型1025.fig, 607928 , 2018-06-28
沙威_OMP\结果图\模型256\模型256稀疏度47.5.fig, 42978 , 2018-06-20
沙威_OMP\结果图\模型256\稀疏度126.jpg, 15444 , 2018-07-01
沙威_OMP\结果图\模型256\稀疏度126小波.jpg, 15921 , 2018-07-01
沙威_OMP\结果图\模型256, 0 , 2018-07-01
沙威_OMP\DCT, 0 , 2018-06-29
沙威_OMP\结果图, 0 , 2018-07-01
沙威_OMP, 0 , 2018-07-07

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    模糊C均值分类,用于图像处理中 很好用的,极力推荐 (Fuzzy C-means classification, used for image processing in a very good use, and strongly recommend)
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    积分:1
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    本程序是一幅512*512的lena.raw图像进行快速DCT和其逆变换,整个程序均调试通过,是在C语言环境下编写的,在整个关于快速DCT的源码中,本代码可以说是集大成者,很值得一看。(This procedure is mainly of a float or double type of data, rounding, the float data can be positive or negative, can be, take the whole principle of rounding.)
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    2018-10-12 10:45:12下载
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    2009-03-11 14:35:12下载
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    2019-03-18 21:00:32下载
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  • 沙威 OMP
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    2020-07-04 13:40:02下载
    积分:1
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    2008-04-13 14:50:04下载
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    主成分分析 ( Principal Component Analysis , PCA )或者主元分析。是一种掌握事物主要矛盾的统计分析方法,它可以从多元事物中解析出主要影响因素,揭示事物的本质,简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值,形成一个 n ′ m 的数据矩阵, n 通常比较大。对于一个由多个变量描述的复杂事物,人们难以认识,那么是否可以抓住事物主要方面进行重点分析呢?如果事物的主要方面刚好体现在几个主要变量上,我们只需要将这几个变量分离出来,进行详细分析。但是,在一般情况下,并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面, PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)
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