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说说wavedec2函数【图】
08-10栏目：技术
TAG：wavedec2
wavedec2
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wavedec2函数:
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1.功能:实现图像(即二维信号)的多层分解.
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多层,即多尺度. www.jhua.org
2.格式:[c,s]=wavedec2(X,N,’wname’) www.jhua.org
[c,s]=wavedec2(X,N,Lo_D,Hi_D)(我不讨论它) jhua.org
3.参数说明:对图像X用wname小波基函数实现N层分解,
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这里的小波基函数应该根据实际情况选择,具体选择办法可以搜之.输出为c,s.
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c为各层分解系数,s为各层分解系数长度,也就是大小. www.jhua.org
4.c的结构:c=[A(N)|H(N)|V(N)|D(N)|H(N-1)|V(N-1)|D(N-1)|H(N-2)|V(N-2)|D(N-2)|…|H(1)|V(1)|D(1)]
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可见,c是一个行向量,即:1*(size(X)),(e.g,X=256*256,then c大小为:1*(256*256)=1*65536) www.jhua.org
A(N)代表第N层低频系数,H(N)|V(N)|D(N)代表第N层高频系数,分别是水平,垂直,对角高频,以此类推,到H(1)|V(1)|D(1). https://www.jhua.org
s的结构:是储存各层分解系数长度的,即第一行是A(N)的长度,第二行是H(N)|V(N)|D(N)|的长度,第三行是 www.jhua.org
H(N-1)|V(N-1)|D(N-1)的长度,倒数第二行是H(1)|V(1)|D(1)长度,最后一行是X的长度(大小)
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那么S有什么用呢？ copyright www.jhua.org
s的结构:是储存各层分解系数长度的,即第一行是A(N)的长度(其实是A(N)的原矩阵的行数和列数),
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第二行是H(N)|V(N)|D(N)|的长度,
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第三行是
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H(N-1)|V(N-1)|D(N-1)的长度, copyright jhua.org
倒数第二行是H(1)|V(1)|D(1)长度, www.jhua.org
最后一行是X的长度(大小) copyright www.jhua.org
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从上图可知道：cAn的长度就是32*32，cH1、cV1、cD1的长度都是256*256。 https://www.jhua.org
到此为止，你可能要问C的输出为什么是行向量?
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1、没有那一种语言能够动态输出参数的个数，更何况C语言写的Matlab
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2、各级详细系数矩阵的大小(size)不一样，所以不能组合成一个大的矩阵输出。 jhua.org
因此，把结果作为行向量输出是最好，也是唯一的选择。
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另:MATLAB HELP 里面说得非常明白了,呵呵. www.jhua.org
wavedec2 copyright jhua.org
Multilevel 2-D wavelet decomposition Syntax [C,S] = wavedec2(X,N,’wname’)
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[C,S] = wavedec2(X,N,Lo_D,Hi_D)
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Description wavedec2 is a two-dimensional wavelet analysis function. copyright jhua.org
[C,S] = wavedec2(X,N,’wname’) returns the wavelet decomposition of the matrix X at level N, using the wavelet named in string ‘wname’ (see wfilters for more information). copyright www.jhua.org
Outputs are the decomposition vector C and the corresponding bookkeeping matrix S. N must be a strictly positive integer (see wmaxlev for more information).
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Instead of giving the wavelet name, you can give the filters. For [C,S] = wavedec2(X,N,Lo_D,Hi_D), Lo_D is the decomposition low-pass filter and Hi_D is the decomposition high-pass filter. www.jhua.org
Vector C is organized as C = [ A(N) | H(N) | V(N) | D(N) | … H(N-1) | V(N-1) | D(N-1) | … | H(1) | V(1) | D(1) ]. where A, H, V, D, are row vectors such that A = APProximation coefficients H = horizontal detail coefficients V = vertical detail coefficients
D = diagonal detail coefficients Each vector is the vector column-wise storage of a matrix.
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Matrix S is such that S(1,:) = size of approximation coefficients(N) S(i,:) = size of detail coefficients(N-i+2) for i = 2, …N+1 and S(N+2,:) = size(X) copyright jhua.org
examples% The current extension mode is zero-padding (see dwtmode). jhua.org
% Load original image. jhua.org
load woman; jhua.org
% X contains the loaded image. https://www.jhua.org
% Perform decomposition at level 2 jhua.org
% of X using db1. copyright www.jhua.org
[c,s] = wavedec2(X,2,’db1′);
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% Decomposition structure organization.
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sizex = size(X)
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sizex =
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256 256
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sizec = size(c) copyright www.jhua.org
sizec =
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1 65536 www.jhua.org
val_s = s
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val_s =
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64 64 www.jhua.org
64 64
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128 128 www.jhua.org
256 256 jhua.org
Algorithm For images, an algorithm similar to the one-dimensional case is possible for two-dimensional wavelets and scaling functions obtained from one-dimensional ones by tensor product. This kind of two-dimensional DWT leads to a decomposition of approximation
coefficients at level j in four components: the approximation at level j+1, and the details in three orientations (horizontal, vertical, and diagonal). The following chart describes the basic decomposition step for images: So, for J=2, the two-dimensional
wavelet tree has the form See Alsodwt, waveinfo, waverec2, wfilters, wmaxlev referencesDaubechies, I. (1992), Ten lectures on wavelets, CBMS-NSF conference series in applied mathematics. SIAM Ed. Mallat, S. (1989), “A theory for multiresolution signal decomposition:
the wavelet representation,” IEEE Pattern Anal. and Machine Intell., vol. 11, no. 7, pp. 674-693. Meyer, Y. (1990), Ondelettes et opérateurs, Tome 1, Hermann Ed. (English translation: Wavelets and operators, Cambridge Univ. Press. 1993.
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析构函数
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