外文翻译文档格式.docx

1、The traditional signal theory, is built on the basis of the analysis of Fourier, Fourier transform is a kind of global change, it has some limitations. In practical application, the people start to Fourier transform are improved, thus resulting in wavelet analysis. Wavelet analysis is a new branch o

2、f mathematics, it is a universal function, Fourier analysis, harmonic analysis, numerical analysis of the most perfect crystalline; in the fields of application, especially in signal processing, image processing, speech processing and nonlinear science domain, it is considered to be the Fourier anal

3、ysis after another effective when frequency analysis method. Wavelet transform and Fourier transform, is a time and frequency domain of the local transform which can effectively extracted from the signal information, through dilation and shift operation function to function or signal multiscale anal

4、ysis （ Multiscale Analysis ）, to solve the Fourier transform can not solve many difficult problemsWavelet transform is a rapid development and more popular signal analysis method, the image processing is a very important application, including image compression, image denoising, image fusion, image

5、decomposition, image enhancement. Wavelet analysis is the analysis method of thinking in the development and continuation. In addition to continuous wavelet, discrete wavelet transform （ CWT ） （ DWT ）, and the wavelet packet （ Wavelet Packet ） and multidimensional waveletWavelet analysis in image pr

6、ocessing applications are very important, including image compression, image denoising, image fusion, image decomposition, image enhancement. Wavelet transform is a new transform analysis method, it has inherited and developed the STFT localization of thought, and also overcomes the window size does

7、 not vary with frequency and other shortcomings, to provide a frequency changing with time frequency window, is a time-frequency signal analysis and processing the ideal tool. It is mainly characterized by transform can highlight some aspects of characteristics, therefore, the wavelet transform in m

8、any areas have been successfully applied, especially wavelet transform discrete digital algorithm has been widely used in many of the problems of the transformation research. Since then, the wavelet transform is more and more the introduction of peoples attention, its application fields more and mor

9、e widely.2, problem overview（ a ） the application of wavelet analysis and developmentThe application of wavelet analysis and wavelet analysis theory to work closely together. Now, it has been in the information technology industry has made the achievement attract peoples attention. Electronic inform

10、ation technology is the six new and high technology an important field, which is an important aspect of image and signal processing. Nowadays, signal processing has become an important part of the work of contemporary science and technology, the purpose of signal processing is: accurate analysis, di

11、agnosis, coding and quantization, fast transmission or storage, accurately reconstruct （ or return ）. From a mathematical perspective, signal and image processing can be unified as a letterCourse number processing （ image can be viewed as a two-dimensional signal ）, the wavelet analysis of the many

12、analysis for many applications, can be attributed to the signal processing problems. Now, for its properties with time stable signal （ stationary random process ）, an ideal tool in processing is still a Fourier analysis. But in the practical application of the vast majority of signal is unstable （ n

13、on stationary random process ）, and is especially suitable for the unstable signal wavelet analysis tool is.In fact the wavelet analysis applied field is very extensive, it includes many disciplines: mathematics; signal analysis, image processing; quantum mechanics, theoretical physics; military ele

14、ctronic warfare and weapons computer intelligent; classification and recognition; music and language artificial synthesis; medical imaging and diagnosis; seismic data processing; mechanical the fault diagnosis and so on; for example, in mathematics, it has been used in numerical analysis, structure,

15、 fast numerical method of curve and surface structure, solving differential equations, control theory. In signal analysis, noise filtering, compression, transmission and so on. In the image processing of the image compression, classification, identification and diagnosis, such as the decontamination

16、. In medical imaging, the reduction of B ultrasound, CT nuclear magnetic resonance imaging time, improve the resolution（1） application of wavelet analysis in signal and image compression wavelet analysis is an important application of the. It is characterized by high compression ratio, compression s

17、peed, the compressed signal can be maintained and image feature invariant, and the transfer of anti interference. The compression method based on wavelet analysis, comparative success of wavelet packet best base method, wavelet texture model method, wavelet transform Zerotree compression, wavelet tr

18、ansform vector compression. （2） the wavelet in the signal analysis are widely used. It can be used for boundary processing and filtering, time-frequency analysis, signal-noise separation and extraction of weak signal, fractal index, signal recognition and diagnosis as well as the multi-scale edge de

19、tection. In conclusion, because wavelet has low entropy, multi-resolution, decorrelation, selected medium characteristics such as flexibility, the theory of wavelet in denoising fields by many scholars, and obtained good results. But how to take certain technology to eliminate image noise while pres

20、erving image detail is an important topic in the image pretreatment. At present, based on wavelet analysis in image denoising image denoising technology has become an important method.（b） in the image processing field, wavelet transform has the following advantages:（1） wavelet decomposition can cove

21、r the whole frequency domain （ provides a mathematically complete description）（2） wavelet transform by selecting appropriate filter, can greatly reduce or remove the correlation between different feature extraction（3） wavelet transform has a zoom characteristics, in the low frequency band can be use

22、d with high frequency resolution and low resolution （ width analysis window ）, in the high frequency band, the available low frequency resolution and high temporal resolution （ narrow analysis window ）（4） wavelet transform on a fast algorithm （ Mallat algorithm ）Wavelet analysis has become one of th

23、e fastest and most attract sb.s attention on one of the subjects, or applied to the field of information involved in almost all disciplines.（c） demonstration programThis paper based on the wavelet transform image denoising methods carried out in-depth research and analysis, the paper introduces seve

24、ral classic wavelet transform denoising method. The wavelet transform modulus maximum denoising method, described in detail the denoising principle and algorithm, analyzes the denoising process parameter selection problem, and gives some basis; detailed correlation of wavelet coefficient denoising m

25、ethod principle and algorithm; wavelet transform threshold denoising method principle and several key the problem is discussed in detail. The last of these methods are analyzed and compared, and discusses their respective advantages and disadvantages and applicable conditions, and gives the simulati

26、on results.In many image denoising based on wavelet transform method, is the largest use wavelet shrinkage denoising method. The traditional hard threshold function and soft threshold function de-noising method in practice has been widely used, and achieved good results. But the hard threshold funct

27、ion is discontinuous resulting reconstructed signal prone to false phenomenon of Gibbs; and the soft threshold function although the overall good continuity, but the estimated value and the actual value of aggregate in the presence of constant deviation, with certain limitations. In view of this, th

28、is paper puts forward a method based on wavelet multiresolution analysis and minimum mean square error criterion for adaptive threshold denoising algorithm. The method uses wavelet threshold de-noising principle, based on minimum mean square error algorithm of LMS and Stein unbiased estimates of the

29、 premise, the derivation of a continuous derivative with multiple threshold function, the use of the threshold for iterative operation, get the optimal threshold, resulting in better image denoising effect. Finally, the simulation results can be seen, the denoising effect is remarkable, and hard thr

30、eshold, soft threshold method is compared, the SNR improvement more, at the same time denoising can preserve image details, is an effective method for image denoising.Wavelet basis function from the following3 aspects to consider.（1）complex and real wavelet selectionComplex wavelet analysis can not only obtain the amplitude information, can also be obtained from the phase information, so the complex wavele