CDF 9/7离散小波变换（卷积）

Question

我试图编写一个简单的自包含程序，使用cdf9/7小波对一维列表进行单级离散小波变换，然后重建它。我只是用卷积/滤波器组方法来了解它的工作原理。换言之，用滤波器卷积列表以获得尺度系数，用不同的滤波器卷积列表以获得小波系数，但仅从其他元素开始。然后向上采样（即在元素之间加零），对小波系数和尺度系数应用滤波器，将它们相加，得到原始列表。在

我可以让Haar小波滤波器工作，但是当我尝试使用cdf9/7滤波器时，它不会产生相同的输入。但是，结果列表和原始列表的总和是相同的。在

我敢肯定这是卷积中一个非常愚蠢的错误，但我就是搞不懂。我尝试过卷积的一系列排列，比如把过滤器放在索引“I”上，而不是从它的左边缘开始，但似乎没有任何效果。。。这可能是其中一个虫子，当我弄清楚的时候，会让我拍拍我的头。在

代码如下：

import random
import math
length = 128
array = list()
row = list()
scaleCoefficients = list()
waveletCoefficients = list()
reconstruction = list()

def upsample(lst, index):
    if (index % 2 == 0):
        return 0.0
    else:
        return lst[index/2]

for i in range(length):
    array.append(random.random())

## CDF 9/7 Wavelet (doesn't work?)
DWTAnalysisLowpass = [.026749, -.016864, -.078223, .266864, .602949, .266864, -.078223, -.016864, .026749]
for i in range(len(DWTAnalysisLowpass)):
    DWTAnalysisLowpass[i] = math.sqrt(2.0) * DWTAnalysisLowpass[i]
DWTAnalysisHighpass = [0.0, .091272, -.057544, -0.591272, 1.115087, -.591272, -.057544, .091272, 0.0]
for i in range(len(DWTAnalysisHighpass)):
    DWTAnalysisHighpass[i] = 1.0/math.sqrt(2.0) * DWTAnalysisHighpass[i]

DWTSynthesisLowpass = [0.0, -.091272, -.057544, 0.591272, 1.115087, .591272, -.057544, -.091272, 0.0]
for i in range(len(DWTSynthesisLowpass)):
    DWTSynthesisLowpass[i] = 1.0/math.sqrt(2.0) * DWTSynthesisLowpass[i]
DWTSynthesisHighpass = [.026749, .016864, -.078223, -.266864, .602949, -.266864, -.078223, .016864, .026749]
for i in range(len(DWTSynthesisHighpass)):
    DWTSynthesisHighpass[i] = math.sqrt(2.0) * DWTSynthesisHighpass[i]

## Haar Wavelet (Works)
## c = 1.0/math.sqrt(2)
## DWTAnalysisLowpass = [c,c]
## DWTAnalysisHighpass = [c, -c]
## DWTSynthesisLowpass = [c, c]
## DWTSynthesisHighpass = [-c, c]


## Do the forward transform - we only need to do it on half the elements
for i in range(0,length,2):
    newVal = 0.0
    ## Convolve the next j elements
    for j in range(len(DWTAnalysisLowpass)):
        index = i + j
        if(index >= length):
            index = index - length

        newVal = newVal + array[index]*DWTAnalysisLowpass[j]

    scaleCoefficients.append(newVal)

    newVal = 0.0
    for j in range(len(DWTAnalysisHighpass)):
        index = i + j
        if(index >= length):
            index = index - length

        newVal = newVal + array[index]*DWTAnalysisHighpass[j]

    waveletCoefficients.append(newVal)

## Do the inverse transform
for i in range(length):
    newVal = 0.0
    for j in range(len(DWTSynthesisHighpass)):
        index = i + j
        if(index >= length):
            index = index - length

        newVal = newVal + upsample(waveletCoefficients, index)*DWTSynthesisHighpass[j]

    for j in range(len(DWTSynthesisLowpass)):
        index = i + j
        if(index >= length):
            index = index - length

        newVal = newVal + upsample(scaleCoefficients, index)*DWTSynthesisLowpass[j]

    reconstruction.append(newVal)

print sum(reconstruction)
print sum(array)
print reconstruction
print array

顺便说一下，我从这里的附录中获取了过滤器值：http://www1.cs.columbia.edu/~rso2102/AWR/Files/Overbeck2009AWR.pdf，但我也看到它们在一堆matlab示例代码中使用过。在

Answer 1

实际上，我自己解决了这个问题，通过比较系数，然后重建，与这个提升实现的代码：

http://www.embl.de/~gpau/misc/dwt97.c

基本上，我 1） 使边界条件对称，而不是周期性的 2） 必须以一定的方式抵消卷积（和上采样）以使其对齐。在

下面是代码，以防其他人遇到问题。我觉得这仍然过于复杂化了，特别是因为它在任何地方都没有真正的文档记录，但至少它是有效的。这也包括我用来对照那个参考进行测试的“开关”，我必须修改Haar小波使其工作。在

import random
import math
length = int()
array = list()
row = list()
scaleCoefficients = list()
waveletCoefficients = list()
reconstruction = list()
switch = False

def upsample1(lst, index):
    if (index % 2 == 0):
        return lst[index/2]
    else:
        return 0.0

def upsample2(lst, index):
    if (index % 2 == 0):
        return 0.0
    else:
        return lst[index/2]

## Generate a random list of floating point numbers
if (not switch):
    length = 128
    for i in range(length):
        array.append(random.random())
else:
    length = 32
    for i in range(32):
        array.append(5.0+i+.4*i*i-.02*i*i*i)

## First Part Just Calculates the Filters
## CDF 9/7 Wavelet
DWTAnalysisLowpass = [.026749, -.016864, -.078223, .266864, .602949, .266864, -.078223, -.016864, .026749]
for i in range(len(DWTAnalysisLowpass)):
    DWTAnalysisLowpass[i] = math.sqrt(2.0) * DWTAnalysisLowpass[i]
DWTAnalysisHighpass = [.091272, -.057544, -0.591272, 1.115087, -.591272, -.057544, .091272]
for i in range(len(DWTAnalysisHighpass)):
    DWTAnalysisHighpass[i] = DWTAnalysisHighpass[i]/math.sqrt(2.0)

DWTSynthesisLowpass = [-.091272, -.057544, 0.591272, 1.115087, .591272, -.057544, -.091272]
for i in range(len(DWTSynthesisLowpass)):
    DWTSynthesisLowpass[i] = DWTSynthesisLowpass[i]/math.sqrt(2.0)
DWTSynthesisHighpass = [.026749, .016864, -.078223, -.266864, .602949, -.266864, -.078223, .016864, .026749]
for i in range(len(DWTSynthesisHighpass)):
    DWTSynthesisHighpass[i] = math.sqrt(2.0) * DWTSynthesisHighpass[i]

## Haar Wavelet
## c = 1.0/math.sqrt(2)
## DWTAnalysisLowpass = [c,c]
## DWTAnalysisHighpass = [c, -c]
## DWTSynthesisLowpass = [-c, c]
## DWTSynthesisHighpass = [c, c]

# Do the forward transform. We can skip every other sample since they would
# be removed in the downsampling anyway
for i in range(0,length,2):
    newVal = 0.0
    ## Convolve the next j elements by the low-pass analysis filter
    for j in range(len(DWTAnalysisLowpass)):
        index = i + j - len(DWTAnalysisLowpass)/2
        if(index >= length):
            index = 2*length - index - 2
        elif (index < 0):
            index = -index

        newVal = newVal + array[index]*DWTAnalysisLowpass[j]

    # append the new value to the list of scale coefficients
    scaleCoefficients.append(newVal)

    newVal = 0.0
    # Convolve the next j elements by the high-pass analysis filter
    for j in range(len(DWTAnalysisHighpass)):
        index = i + j - len(DWTAnalysisHighpass)/2 + 1
        if(index >= length):
            index = 2*length - index - 2
        elif (index < 0):
            index = -index

        newVal = newVal + array[index]*DWTAnalysisHighpass[j]

    # append the new value to the list of wavelet coefficients
    waveletCoefficients.append(newVal)

# Do the inverse transform
for i in range(length):
    newVal = 0.0
    # convolve the upsampled wavelet coefficients with the high-pass synthesis filter
    for j in range(len(DWTSynthesisHighpass)):
        index = i + j - len(DWTSynthesisHighpass)/2
        if(index >= length):
            index = 2*length - index - 2
        elif (index < 0):
            index = -index

        newVal = newVal + upsample2(waveletCoefficients, index)*DWTSynthesisHighpass[j]

    # convolve the upsampled scale coefficients with the low-pass synthesis filter, and
    # add it to the previous convolution
    for j in range(len(DWTSynthesisLowpass)):
        index = i + j - len(DWTSynthesisLowpass)/2
        if(index >= length):
            index = 2*length - index - 2
        elif (index < 0):
            index = -index

        newVal = newVal + upsample1(scaleCoefficients, index)*DWTSynthesisLowpass[j]

    reconstruction.append(newVal)

print ("Sums: ")
print sum(reconstruction)
print sum(array)
print ("Original Signal: ")
print array
if (not switch):
    print ("Wavelet Coefficients: ")
    for i in range(len(scaleCoefficients)):
        print ("sc[" + str(i) + "]: " + str(scaleCoefficients[i]))
    for i in range(len(waveletCoefficients)):
        print ("wc[" + str(i) + "]: " + str(waveletCoefficients[i]))
print ("Reconstruction: ")
print reconstruction