imwdc.object(wavethresh)
imwdc.object()所属R语言包：wavethresh
Two-dimensional compressed wavelet decomposition objects.
二维压缩的小波分解对象。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These are objects of classes
这些类的对象
imwdc
imwdc
They represent a decomposition of an image with respect to a two-dimensional wavelet basis
它们代表了一个相对于图像分解到二维小波基础
Details
详细信息----------Details----------
In previous releases the original image was stored as the "original" component of a imwd object. This is not done now as the resulting objects were excessively large.
在以前的版本中，原始图像存储为“原始”的组成部分的imwd对象。现在不这样做所产生的物件过大。
To uncompress this class of object back into an object of class imwd.object use the uncompress.imwdc function.
要解压缩到一个类的对象，这个类的对象imwd.object使用uncompress.imwdc功能。
值----------Value----------
The following components must be included in a legitimate "imwdc" object.
以下组件必须被包括在一个的合法imwdc对象。
<table summary="R valueblock"> <tr valign="top"><td>nlevels</td> <td> number of levels in wavelet decomposition. If you raise 2 to the power of nlevels then you get the dimension of the image that you originally started with. </td></tr> <tr valign="top"><td>type</td> <td> If type="wavelet" then the image was decomposed according to the 2D Mallat pyramidal algorithm. If type="station" then the image was decomposed using the 2D spatially ordered non-decimated wavelet transform.</td></tr> <tr valign="top"><td>fl.dbase</td> <td> The first last database associated with the decomposition. For images, this list is not very useful as each level's components is stored as a list component, rather than being packaged up in a single vector as in the 1D case. Nevertheless the internals still need to know about fl.dbase to get the computations correct. See the help for first.last if you are a masochist. </td></tr> <tr valign="top"><td>filter</td> <td> A filter object as returned by the filter.select function. This component records the filter used in the decomposition. The reconstruction routines use this component to find out what filter to use in reconstruction. </td></tr> <tr valign="top"><td>wNLx</td> <td> The object will probably contain many components with names of this form. These are all the wavelet coefficients of the decomposition. In "wNLx" the "N" refers to the level number and the "x" refers to the direction of the coefficients with "1" being horizontal, "2" being vertical and "3" being diagonal. Note that imwdc objects do not contain scaling function coefficients. This would negate the point of having a compressed object.
<table summary="R valueblock"> <tr valign="top"> <TD>nlevels</ TD> <TD>在小波分解的层次。如果你提高2的力量NLEVELS的，那么你的形象，你最初的尺寸。 </ TD> </ TR> <tr valign="top"> <TD> type </ TD> <TD>如果type="wavelet"然后将图像分解的二维Mallat的金字塔算法。如果type="station"然后将图像分解二维空间下令非抽取小波变换。</ TD> </ TR> <tr valign="top"> <TD> fl.dbase</ TD> <TD>的第一个相关联的数据库的分解。对于图像来说，这份名单是不是非常有用，因为每个级别的存储组件的列表组件，而不是在一维的情况下，被打包成一个向量。然而，内部仍然需要知道关于fl.dbase的计算是正确的。请参阅帮助first.last如果你是受虐狂。 </ TD> </ TR> <tr valign="top"> <TD> filter </ TD> <td>一个过滤器作为返回filter.select功能的对象。此组件记录在分解中使用的过滤器。重建程序使用这个组件，以找出什么样的滤波器中使用的重建。 </ TD> </ TR> <tr valign="top"> <TD>wNLx</ TD> <TD>的对象可能会包含许多组件，这种形式的名称。这些所有的小波系数的分解。 “N”指在“wNLx”级别号的“x”指的方向的系数中包含“1”的水平，“2”的垂直和“3”是对角线。请注意，imwdc对象不包含缩放函数系数。这将否定点有一个压缩的对象。
Each vector stores its coefficients using an object of class compressed, i.e. the vector is run-length encoded on zeroes.
每个向量的系数存储，使用一个对象类的压缩，即该向量是在零游程长度编码。
Note that the levels should be in numerically decreasing order, so if nlevels is 5, then there will be w5L1, w5L2, w5L3 first, then down to w1L1, w1L2, and w1L3. Note that these coefficients store their data according to the first.last database fl.dbase$first.last.d, so refer to them using this.
注意：水平应该是在数字递减的顺序，，所以如果NLEVELS是5，那么就会是w5L1，w5L2，w5L3，然后下降到W1L1，w1L2，w1L3。请注意，这些系数将其数据存储的first.last数据库fl.dbase$first.last.d，所以，请参阅使用。
Note that if type="wavelet" then images at level N are subimages of side length 2^N pixels. If the type component is "station" then each coefficient subimage is of the same dimension as the input image used to create this object.</td></tr> <tr valign="top"><td>w0Lconstant</td> <td> This is the coefficient of the bottom level scaling function coefficient. So for examples, if you used Haar wavelets this would be the sample mean of the data (scaled by some factor depending on the number of levels, nlevels).</td></tr> <tr valign="top"><td>bc</td> <td> This component details how the boundaries were treated in the decomposition.</td></tr> </table>
请注意，如果type="wavelet"然后图像在第N级子图像的边长2^N像素。如果该类型组件是"station"然后每个子图是用于创建此对象的输入图像尺寸相同的系数。</ TD> </ TR> <tr valign="top"> <TD>w0Lconstant </ TD> <td>这是最底层的尺度函数系数系数。所以的例子，如果你使用Haar小波这将是样本平均数的数据缩放的水平，NLEVELS的数量取决于一些因素。</ TD> </ TR> <tr valign="top"> TD> bc </ TD> <td>此组件的详细信息分解的界限如何处理。</ TD> </ TR> </ TABLE>
GENERATION----------GENERATION----------
This class of objects is returned from the threshold.imwd function to represent a thresholded two-dimensional wavelet decomposition of a function. Some other functions return an object of class imwdc.
从threshold.imwd函数来表示一个阈值的二维小波分解函数将返回这个类的对象。其他一些功能会传回一个对象类imwdc。
方法----------METHODS----------
The imwd class of objects has methods for the following generic functions: draw, imwr, nullevels, plot, print, summary, threshold.imwdc.
的imwd类的对象有以下通用功能的方法：draw，imwr，nullevels，plot，print，summary， threshold.imwdc。
RELEASE----------RELEASE----------
Version 3.5.3 Copyright Guy Nason 1994
版本3.5.3版权盖利晨1994年
（作者）----------Author(s)----------
G P Nason
参见----------See Also----------
imwd imwd.object, threshold.imwd, uncompress.imwdc.
imwdimwd.object，threshold.imwd，uncompress.imwdc。
实例----------Examples----------
#[]
# Perform the standard two-dimensional DWT[执行标准二维DWT]
# on the lennon image.[列侬的形象。]
#[]
data(lennon)
lwd <- imwd(lennon)
#[]
# Now let's see how many horizontal detail coefficients there are at[现在，让我们来看看有多少水平细节系数]
# scale 6[规模6]
#[]
length(lwd$w6L1)
# [1] 4096[[1] 4096]
#[]
# So the horizontal detail ``image'' at scale contains 64x64=4096 coefficients.[因此，水平细节“图像”在规模包含64×64 = 4096系数。]
# A lot![很多！]
#[]
# Now, suppose we threshold this[现在，让我们假设我方不同意时的阈值这]
# two-dimensional wavelet decomposition object[二维小波分解对象]
#[]
lwdT <- threshold(lwd)
#[]
# First of all. What is the class of the detail coefficients now? [首先。现在是什么之类的细节系数？]
#[]
class(lwdT$w6L1)
# [1] "compressed"[[1]“压缩”]
#[]
# Aha. So this set of coefficients got compressed using the[啊哈。因此，这组系数得到了压缩使用]
# compress.default function.[compress.default功能。]
#[]
# How many coefficients are being stored here?[多少系数被存储在这里吗？]
#[]
lwdT$w6L1
# $position:[$的位置：]
# [1] 173 2829 2832 2846[[1] 173 2829 2832 2846]
#[]
# $values:[$值：]
# [1] 141.5455 -190.2810 -194.5714 -177.1791[[1] 141.5455 -190.2810 -194.5714 -177.1791]
# []
# $original.length:[$ original.length：]
# [1] 4096[[1] 4096]
#[]
# attr(, "class"):[ATTR（“类”）：]
# [1] "compressed"[[1]“压缩”]
#[]
# Wow! Only 4 coefficients are not zero. Wicked compression![哇！只有4个系数不为零。邪恶的压缩！]
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 17:21:59
