基于微分算子的Eno-haar小波变换及其应用
The Eno-haar Wavelet Transforms Based on Differential Operators and Its Application
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摘要: 该文首先引入微分算子并结合Haar小波的特点，提出了一种遗传性算法，用于2D信号奇异性检测。其次，将该算法与Eno-haar(Essentially non-oscillatory-haar)小波相结合，得到了一种基于微分算子的Eno-haar小波变换算法，并通过仿真实验说明了其在图像压缩中的可行性和有效性。Abstract: In this paper, the differential operators are introduced firstly. Then based on the characteristics of Haar wavelet transforms and the differential operators, a transmissibility algorithm is proposed and applied to the singularity measuring of 2D signal. Secondly, a new algorithm called the Eno-haar (Essentially non-oscillatory-haar) wavelet transforms algorithm based on the differential operators is presented. And it is proved by experiments that this algorithm is effective and feasible to image compression.
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Hao-Min Zhou.Wavelet transforms and PDE techniques in image compression [D].University of California,2000.[2]Donobo D.De-noising by soft thresholding [J].IEEE Trans.on Info.Theory,1995,41(3):612-627.[3]Ingrid Daubechies.Ten Lectures on Wavelets [M].Philadelphia,Pennsylvania,SIAM,1992,Chap.1～Chap.5.[4]章毓晋.图象分割[M].北京:科学出版社,2001,第二章.[5]Kirsch R.Computer determination of the constituent structure of biological images [J].Computer Biomedical Research.1971,4(3):315-328 -
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