Description: 小波去噪,利用小波系数在相邻尺度上的相关性关系,针对小波系数估计中硬阈值方法和软阈值方法的缺点,
通过对双重量收缩函数得到的阈值乘以一个合适系数进行修定的折衷方法,提出了一种新的小波域局部自适应去噪算法.实验结果表明,该方法既可以去除噪声,又可以较好地保留图像的高频细节特征.-Wavelet denoising using the wavelet coefficients in adjacent scales of the correlation relationship between the estimation of wavelet coefficients for the hard thresholding and soft thresholding shortcomings, through the contraction of the double weight of the threshold function has been multiplied by a suitable coefficient revised compromise, proposed a new wavelet-domain partially adaptive denoising algorithm. The experimental results show that the method can remove noise, also can be used to retain the details of the characteristics of high-frequency images. Platform: |
Size: 3072 |
Author:weiwei |
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Description:
Wavelet denoising
For using this code need to use signal toolbox and general toolbox in your matlab
In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size
512 by 512 and then generate noisy image by adding a Gaussian noise but under the condition of
having SNR=20dB by select the suitable value of variance for Gaussian noise formula.
Second step is performing wavelet denoising using the hard thresholding (Use the db 6 for four
levels) in the condition of finding the optimal thresholding value of T in terms of the SNR obtained. It
means that, we should find the highest SNR value by finding the suitable value for threshold. Then we
asked to do the same process but this time using soft thresholding.
Finally for the last part of question one, we should compare the results of the obtained SNR
with the recommendations of 3*sigma for the hard thresholding and 3/2*sigma for the soft
thresholding.-
Wavelet denoising
For using this code need to use signal toolbox and general toolbox in your matlab
In the first part of this assignment, we asked to obtain a (black-and-white) digital image of size
512 by 512 and then generate noisy image by adding a Gaussian noise but under the condition of
having SNR=20dB by select the suitable value of variance for Gaussian noise formula.
Second step is performing wavelet denoising using the hard thresholding (Use the db 6 for four
levels) in the condition of finding the optimal thresholding value of T in terms of the SNR obtained. It
means that, we should find the highest SNR value by finding the suitable value for threshold. Then we
asked to do the same process but this time using soft thresholding.
Finally for the last part of question one, we should compare the results of the obtained SNR
with the recommendations of 3*sigma for the hard thresholding and 3/2*sigma for the soft
thresholding. Platform: |
Size: 91136 |
Author:jams1166 |
Hits:
Search - soft thresholding in matlab