Image Denoising, Based On The Proportion Of Dyadic Wavelet Coefficients Shrink

The proportion-shrinking denoising algorithm based on wavelet transform is a better image denoising method, which has the abilities to adapt to the local signals and is more flexible than the wavelet threshold denoising method. In this thesis, we introduce an algorithm of proportion-shrinking denoising based on Minimum Mean Square Error(MMSE) and make some further improvements. On the base of these improvements, we propose an algorithm of proportion-shrinking based on correlation coefficient of dyadic wavelet.The chief improvements are as follows:Firstly, the dyadic wavelet transform is used to deal with images. Since down-sampling is not used in image dyadic wavelet transform at each decomposition level, the representation is redundant compared with wavelet domain series, and the disturbance of the part coefficients in image dyadic wavelet transform does not lead to serious distortion. Therefore, with the same inaccurate estimation probability, the denoising effect based on dyadic wavelet transform is better.Secondly, the signal belief is defined by using wavelet correlation coefficient and standardization correlation coefficient. Further, the images are denoised by using signal belief as a measurement.Simulation experiments show that the algorithm based on correlation coefficient is simpler than traditional method of Proportion-shrinking. At the same time in removing the noise, the important features of image can be preserved effectively.