Research on Image Denoising Based on Wavelet Adaptive Threshold
YU Du-f · Computer Technology and Development · 2013
Using wavelet transform to filter noises on image is a very effective method. The smoothing effect is not very good of traditional wavelet image denoising algorithm,and the image detail precision isn't high enough,even false Gibbs phenomenon can be produced.Aimming at the phenomenon,an improved multi-scale adaptive threshold method of image denoising based on wavelet transformation has been proposed. According to the characteristics of the image wavelet decomposition,this method can determine the better threshold of different layers' coefficient for denoising after wavelet decomposition,then process the high frequency coefficient of each layer with appropriate threshold function to achieve denoising effect. The experimental results show that,compared with traditional methods,this method can effectively remove Gaussian white noise and further improve the peak signal-to-noise ratio,while well preserving image details.