imwd(wavethresh)
imwd()所属R语言包：wavethresh
Two-dimensional wavelet transform (decomposition).
二维小波变换（分解）。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function can perform two types of two-dimensional discrete wavelet transform (DWT). The standard transform (type="wavelet") computes the 2D DWT according to Mallat's pyramidal algorithm (Mallat, 1989). The spatially ordered non-decimated 2D DWT (NDWT) (type="station") contains all possible spatially shifted versions of the DWT. The order of computation of the DWT is O(n), and it is O(n log n) for the NDWT if n is the number of pixels.
此功能可以执行两维离散小波变换（DWT）的两种类型的。标准的变换（type="wavelet"）计算2D载重吨，根据Mallat的锥体算法（Mallat的，1989年）。空间有序的非锐减2D载重吨（NDWT）的（type="station"）包含所有可能的空间位移的DWT。的顺序计算的DWT是O（n）的，并且它是O（nlogn）为NDWT如果n的像素的数量。
用法----------Usage----------
imwd(image, filter.number=10, family="DaubLeAsymm", type="wavelet",
bc="periodic", RetFather=TRUE, verbose=FALSE)
参数----------Arguments----------
参数：image
A square matrix containing the image data you wish to decompose. The sidelength of this matrix must be a power of 2.
一方阵包含你想分解的图像数据。此矩阵的sidelength必须是2的幂的。
参数：filter.number
This selects the smoothness of wavelet that you want to use in the decomposition. By default this is 10, the Daubechies least-asymmetric orthonormal compactly supported wavelet with 10 vanishing moments.
选择要使用的分解小波的平滑度。默认情况下，这是10，至少不对称的Daubechies正交的紧支撑小波与10个消失矩。
参数：family
specifies the family of wavelets that you want to use. The options are "DaubExPhase" and "DaubLeAsymm".
指定要使用的小波家庭。的选项“DaubExPhase”和“DaubLeAsymm”。
参数：type
specifies the type of wavelet transform. This can be "wavelet" (default) in which case the standard 2D DWT is performed (as in previous releases of WaveThresh). If type is "station" then the 2D spatially-ordered non-decimated DWT is performed. At present, only periodic boundary conditions can be used with the 2D spatially ordered non-decimated wavelet transform.
指定类型的小波变换。这是“小波”（默认值），在这种情况下，标准的2D DWT（在以前的版本中，WaveThresh）。如果类型是“站”在二维空间上非锐减DWT进行排序。目前，只有周期性的边界条件，可以用二维空间下令非抽取小波变换。
参数：bc
specifies the boundary handling. If bc=="periodic" the default, then the function you decompose is assumed to be periodic on it's interval of definition, if bc=="symmetric" then the function beyond its boundaries is assumed to be a symmetric reflection of the function in the boundary. The symmetric option was the implicit default in releases prior to 2.2. Note that only periodic boundary conditions are valid for the 2D spatially-ordered non-decimated wavelet transform.
指定的边界处理。如果BC ==“定期”的默认，然后分解的功能，您认为是周期性的间隔的定义，当BC ==“对称”，那么的功能超越它的界限，假设是对称反射的功能在边界。对称隐含的默认选项是2.2之前的版本中。请注意，只有周期性的边界条件下是有效的非抽取小波变换的二维空间排序。
参数：RetFather
If TRUE then this argument causes the scaling function coefficients at each resolution level to be returned as well as the wavelet coefficients. If FALSE then no scaling function coefficients are returned. The opportunity of returning father wavelet coefficients has been added since previous versions of WaveThresh.
如果TRUE然后此参数会导致在每个分辨率等级的尺度函数系数以及返回的小波系数。如果FALSE然后没有尺度函数系数返回。已添加的机会，回到父亲的小波系数，因为以前版本的WaveThresh。
参数：verbose
Controls the printing of "informative" messages whilst the computations progress. Such messages are generally annoying so it is turned off by default.
控制打印的“信息”的消息，而计算的进展。这样的消息一般都是讨厌的，所以它在默认情况下是关闭的。
Details
详细信息----------Details----------
The 2D algorithm is essentially the application of many 1D filters. First, the columns are attacked with the smoothing (H) and bandpass (G) filters, and the rows of each of these resultant images are attacked again with each of G and H, this results in 4 images. Three of them, GG, GH, and HG correspond to the highest resolution wavelet coefficients. The HH image is a smoothed version of the original and can be further attacked in exactly the same way as the original image to obtain GG(HH), GH(HH), and HG(HH), the wavelet coefficients at the second highest resolution level and HH(HH) the twice-smoothed image, which then goes on to be further attacked.
的二维算法本质上是许多1D滤波器的应用。首先，列与平滑（H）和带通滤波器（G）的攻击，这些得到的图像中的各行与每个G和H，这结果在4个图像再次攻击。其中三个，GG，GH，和HG对应的最高分辨率的小波系数。该HH图像是一个平滑的版本的原始作为原始图像，得到GG（HH），GH（HH），和HG（HH），在第二最高分辨率的小波系数，并可以进一步在完全相同的方式攻击水平和HH（HH）的两次的平滑后的图像，然后进到作进一步攻击。
If RetFather=TRUE then the results of the HH smooth (the scaling function coefficients) are returned additionally.
如果RetFather=TRUE然后附加的HH（尺度函数系数）光滑的结果返回。
There are now two methods of handling "boundary problems". If you know that your function is periodic (on it's interval) then use the bc="periodic" option, if you think that the function is symmetric reflection about each boundary then use bc="symmetric". If you don't know then it is wise to experiment with both methods, in any case, if you don't have very much data don't infer too much about your decomposition! If you have loads of data then don't worry too much about the boundaries. It can be easier to interpret the wavelet coefficients from a bc="periodic" decomposition, so that is now the default.
现在有两种方法处理“边界问题”。如果你知道你的函数是周期性的间隔，然后使用bc =“定期”选项，如果你认为每个边界，然后使用bc =“对称”的功能是对称的反射。如果你不知道，那么明智的做法是用两种方法进行试验，在任何情况下，如果没有非常多的数据，推断出太多的分解！如果你有大量的数据，那么不用担心太多的界限。它可以更容易解释从BC =“定期”分解的小波系数，所以，现在是默认的。
The spatially-ordered non-decimated DWT contains all spatial (toroidal circular) shifts of the standard DWT.
空间有序的非锐减DWT包含了所有空间（环形圆）的标准DWT转移。
The standard DWT is orthogonal, the spatially-ordered non-decimated transform is most definitely not. This has the added disadvantage that non-decimated wavelet coefficients, even if you supply independent normal noise. This is unlike the standard DWT where the coefficients are independent (normal noise).
标准DWT是正交的，空间有序的非锐减的变换是最绝对不是。这是额外的缺点，非抽取小波系数的，即使你提供独立的正态噪声。这是一个不同于标准的DWT系数是独立的（正常的噪音）。
The two-dimensional packet-ordered non-decimated discrete wavelet transform is computed by the wst2D function.
的两维的数据包的命令非抽取的离散小波变换的计算由wst2D函数。
值----------Value----------
An object of class imwd.object containing the two-dimensional wavelet transform (possibly spatially-ordered non-decimated).
类的一个对象imwd.object包含二维小波变换（可能的空间排列的非抽取）。
RELEASE----------RELEASE----------
Version 3.3 Copyright Guy Nason 1994
版本3.3版权所有盖利晨1994年
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
wd, imwd.object, filter.select
wd，imwd.object，filter.select
实例----------Examples----------
data(lennon)
#[]
# Let's use the lennon test image[让我们的的列侬测试图像使用]
#[]
## Not run: image(lennon)[＃非执行：图像（列侬）]
#[]
# Now let's do the 2D discrete wavelet transform[现在，让我们做二维离散小波变换]
#[]
lwd <- imwd(lennon)
#[]
# Let's look at the coefficients[让我们来看看系数]
#[]
## Not run: plot(lwd)[＃未运行图（LWD）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 17:21:46
