Wavelet transform overcome traditional Fourier transform don't have the time domain information shortcomings, it has good, especially localization performance, through the telescopic translation operations to signal gradually different scales carefully, and finally reach high frequency place time segments, low frequency place frequency subdivision, can automatically adapt to the requirements of time-frequency signal analysis, it can be focused to a signal any detail, solve the difficult problem of Fourier transform, become the Fourier transform in scientific method since breakthroughs. The wavelet theory in the image denoising, image compression, image enhancement, etc widely application. From the basic principle of the first wave, multi-resolution, image wavelet coefficients of wavelet systems analysis, then the image wavelet pyramidal decomposition, get high frequency and low frequency coefficients of wavelet, finally according to the distribution characteristics of wavelet coefficients, respectively, in different evaluation standard (information entropy and mean square error, root mean square error) test the quality of the image coding reconstruction. Wavelet analysis is redundant, after decomposition of the total quantity is not large, and decomposition in different components are orthogonal is, these advantages, wavelet image compression in application of can obtain good effect. And wavelet technology used in image processing speed, also has the characteristics of degeneration etc. Characteristics.
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