AN APPLIED RESEARCH ON APPROACH OF DYADIC WAVELET TRANSFORM FOR REMOTE SENSING IMAGE EDGE DETECTION

In the edge detection of Remote Sensing (RS) image, the useful detail losing and the spurious edge often appear. To solve the problem, the authors uses the dyadic wavelet to detect the edge of surface features by combining the edge detecting with the multi-resolution analyzing of the wavelet transform. Via the dyadic wavelet decomposing, the RS image of a certain appropriate scale is obtained, and the edge data of the plane and the upright directions are respectively figured out, then the gradient vector module of the surface features is worked out. By tracing them, the authors get the edge data of the object, therefore build the RS image which obtains the checked edge. This method can depress the effect of noise and examine exactly the edge data of the object by rule and line. With an experiment of an RS image which obtains an airport, the authors certificate the feasibility of the application of dyadic wavelet in the object edge detection.

COMPUTATION OF CONTINUOUS WAVELET TRANSFORM AT DYADIC SCALES BY SUBDIVISION SCHEME

A new algorithm to compute continuous wavelet transforms at dyadic scales is proposed here. Our approach has a similar implementation with the standard algorithme a trous and can coincide with it in the one dimensional lower order spline case.Our algorithm can have arbitrary order of approximation and is applicable to the multidimensional case.We present this algorithm in a general case with emphasis on splines anti quast in terpolations.Numerical examples are included to justify our theorerical discussion.

Feature Extraction and Classification of Echo Signal of Ground Penetrating Radar

Automatic feature extraction and classification algorithm of echo signal of ground penetrating radar is presented. Dyadic wavelet transform and the average energy of the wavelet coefficients are applied in this paper to decompose and extract feature of the echo signal. Then, the extracted feature vector is fed up to a feed forward muhi layer perceptron classifier. Experimental results based on the measured GPR, echo signals obtained from the Mei shan railway are presented.

A New Pitch Detection Algorithm Based on Wavelet Transform

In this paper, a new event detection pitch detector based on the dyadic wavelet transform was constrcted by selecting an optimal scale. The proposed pitch detector is accurate, robust to noise and computationally simple. Experiments show the superior performance of this event-based pitch detector in comparison with previous event-based pitch detector and classical pitch detectors that use the autocorrelation and the cepsmun methods to estimate the pitch period.

A New Method of CT Medical Images Contrast Enhancement

A new method of contrast enhancement is proposed in the paper using multiscale edge representation of images, and is applied to the field of CT medical image processing. Comparing to the traditional Window technique, our method is adaptive and meets the demand of radiology clinics more better. The clinical experiment results show the practicality and the potential applied value of our method in the field of CT medical images contrast enhancement.

Application of Holter ECG Signal Analysis Based on Wavelet and Data Mining Technique

A new model based on dyadic differential wavelet was developed for detecting the R peak in Holter ECG signal according to the design of data mining. The Mallat recursive filter algorithm was introduced to calculate wavelet and optimize the detection algorithm which is based on the equivalent filter technique. The detection algorithm has been verified by MIT arrhythmia database with a high efficiency of 99%. After optimization, the algorithm was put into clinical experiment and tested in the Air Force Hospital in Tianjin for about two months. After about 108 hearts beating test of more than 100 patients, the total efficient detection rate has reached 97%. Now this algorithm module has been applied in business software and shows perfect performance under the complex conditions such as the inversion of heart beating, the falling off of the electrodes, the excursion of base line and so on.

Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.

Dyadic Bivariate Wavelet Multipliers in L^2（R@2）

The single 2 dilation wavelet multipliers in one-dimensional case and single A-dilation （where A is any expansive matrix with integer entries and [detA[ = 2） wavelet multipliers in twodimen- sional case were completely characterized by Wutam Consortium （1998） and Li Z., et al. （2010）. But there exist no results on multivariate wavelet multipliers corresponding to integer expansive dilation matrix with the absolute value of determinant not 2 in L^2（R^2）. In this paper, we choose 2I2 = （02 20 ） as the dilation matrix and consider the 212-dilation multivariate wavelet ψ = {ψ1, ψ2, ψ3 } （which is called a dyadic bivariate wavelet） multipliers. Here we call a measurable function family f ={fl, f2, f3} a dyadic bivariate wavelet multiplier if ψ1 = （F^-1（f1ψ1）,F^-1（f2ψ2）, F-l（f3ψ3）} is a dyadic bivariate wavelet for any dyadic bivariate wavelet ψ = {ψ1, ψ2, ψ3}, where f and F^- 1 denote the Fourier transform and the inverse transform of function f respectively. We study dyadic bivariate wavelet multipliers, and give some conditions for dyadic bivariate wavelet multipliers. We also give concrete forms of linear phases of dyadic MRA bivariate wavelets.

A new iterative algorithm for reconstructing a signal from its dyadic wavelet transform modulus maxima

A new algorithm for reconstructing a signal from its wavelet transform modulus maxima is presented based on an iterative method for solutions to monotone operator equations in Hilbert spaces. The algorithm's convergence is proved. Numerical simulations for different types of signals are given. The results indicate that compared with Mallat's alternate projection method, the proposed algorithm is simpler, faster and more effective.