图像分析与处理 PROJECT 07-01One-Dimensional Discrete Wavelet Transf

图像分析与处理
PROJECT 07-01
One-Dimensional Discrete Wavelet Transforms
The purpose of this project is to build a rudimentary wavelet transform
package using Haar wavelets that can be used in projects that follow.
You will use an "averaging and differencing" approach that is unique to
Haar basis functions.As an introduction to the method,consider the
function in Example 7.8.The necessary "averaging and differencing"
operations are:
Step 1:Compute two-point sums and differences across the function
vector and divide the results by the square root of 2.Since f(x) = {1,4,
-3,0},we get {1 + 4,-3 + 0,1 - 4,-3 - 0} / 1.414
{5,-3,-3,-3} / 1.414
Note that the sums are positioned consecutively at the beginning of the
intermediate result and followed by the corresponding differences.
Step 2:Repeat the process over the sums computed in the first step to
get {[5 + (-3)] / 1.414,[5 - (-3)] / 1.414,-3,-3} / 1.414
{2 / 1.414,8 / 1.414,-3,-3} / 1.414
{1,4,-2.121,-2.121}
The coefficients of the final vector match those in Example 7.8.The
two-step computation generates a two-scale DWT with respect to Haar
wavelets.It can be generalized to higher scales and functions with more
than 4 points.Moreover,an inverse DWT can be computed by reversing
the process.
(a) Write a program to compute j-scale DWTs with respect to Haar
wavelets.Let scale be an input parameter and assume a 2M point
discrete one-dimensional function.Use the averaging and differencing
approach described above.
(b) Write a program to compute the inverse DWT of a j-scale DWT based
on Haar wavelets.
(c) Test your programs using the function in Example 7.8.
2.PROJECT 08-01
Objective Fidelity Criteria
(a) Write a program to compute the root-mean-square error [see Eq.
(8.1-8)] and meansquare signal-to-noise ratio [per Eq.(8.1-9)] of a
compressed- decompressed image.This project is generic in the sense
that it will be used in other projects that follow.
(b) Download the image of Fig.8.4(a) and write a program to generate
the results in the (b)and (c) parts of the figure.Use your fidelity criteria
program to characterize any loss of visual information and comment on
your results.

不是用C写吧.
matlab的话简单.
[LL,LH,HL,HH]=dwt2(图片,'haar');
反DWT
TuPian=idwt2(LL,LH,HL,HH,'haar');
再问： 呃 我真的是一点都不懂程序 555... 非常感谢您的解答~~ 还请写明程序(加注释)，word文档包括结果（图、数据）、分析 不好意思问来问去的 还有余下的两题呢 一共四道 http://zhidao.baidu.com/question/401563442.html?oldq=1 谢谢~~
再答： lena='c:\lena.bmp';%图片地址 lena=imread(lena);%读取图片 [LL,LH,HL,HH]=dwt2(lena,'db1');%图片做HaarDWT%%dwt2(lena,'db1');db1可替换成haar。 lena2=idwt2(LL,LH,HL,HH,'db1');%后面的db1可以换换看哦，比如db2、db3等等
再问： 请把第2题的答案也帮忙解答吧~~ 谢谢~~