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Wavelet Thresholding Denoising Alogrithm Research Based on the Sure Theory

Author: ZhuWenTao
Tutor: FuZuo
School: Yanshan University
Course: Signal and Information Processing
Keywords: Denoising Wavelet coefficients Threshold Stein unbiased risk estimation The mean square error
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 163
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Abstract


The noise is the important factor affecting the image quality , the presence of noise will cause some of the characteristics of the image detail can not be identified , decreased image SNR , Therefore , in image processing , how to effectively remove the noise , the extracted image information becomes very important . In recent years , based on wavelet transform Denoising an increasingly wide range of research and application . The wavelet denoising is constructed according to the different manifestations of the image signal and the noise and wavelet transform , the corresponding rule, the wavelet transform coefficients for processing the image signal and the noise , the essence of the processing is to minimize or even completely removed by the noise generated coefficient , at the same time maximize the retention of effective image signal corresponding wavelet coefficients . There are a variety of denoising based on wavelet transform method , this article focuses threshold denoising method . First, a detailed introduction of the principle of threshold denoising , a detailed analysis and description of the selected threshold denoising process and the critical parameters such as threshold function , and gives some selected basis ; Secondly , detail wavelet denoising common several methods , including the basic threshold function threshold function based on the statistical model and simulation experiments and compared them . Based on a statistical model of threshold denoising method especially BLS-GSM model , considering all four statistical properties of the wavelet coefficients : sparsity , dissemination , aggregation , and directional , so it denoising results naturally than other denoising the good effect of the algorithm , but this method is the need for the original image assuming a standard probability distribution model , more complicated. Therefore Finally, based on a linear mean square estimation theory , given an improved threshold denoising algorithm , a linear extension of the threshold function . Through the experimental simulation of grayscale images and RGB images show that the algorithm has better denoising effect .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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