一种小波系数模型在图像噪声参数估计中的应用
On the Usage of a Wavelet Coefficient Model in Noise Variance Estimation of Image
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摘要: 在小波图像处理中，通常利用HH子带来估计高斯白噪声方差，目前流行的估计方法是由Donoho和Johnstone提出的(简称DJ法)，但是该方法给出的估计值通常都偏大。针对这一点，该文将他们的方法结合双随机小波系数模型，提出了一种新的、递归的方差估计方法。在已由Donoho的方法获得噪声方差估计的粗略估计的情况下，新方法利用统计学理论将HH子带中的信号滤除从而得到更接近于纯噪声的HH子带，然后利用这一新的HH子带来估计噪声的方差。结合EM参数估计方法，该方法还可以实现非高斯噪声参数的估计，实验表明新方法同Donoho法相比有很大的改善。
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关键词:
- 系数模型; EM算法; 小波变换
Abstract: During wavelet image processing, the variance of Gaussian white noise is usually estimated in the finest HH subband. A popular method, proposed by Donoho and Johnstone, is often found to provide too large an estimate. To tackle this problem, this paper presents a new method. The new method takes the rude estimate from Donohos method as the starting point, and then a subband more dominated by noise is produced with the signal filtered out by a filter derived from statistics theory and a newly-proposed coefRcient model, the doubly stochastic process. Thus a finer estimate is possible by using Donohos method on the filtered HH subband. Through employing EM algorithm, the new method can he straightly extended to the case of non-Gaussian noise. Experimental results show that the new method can improve the estimate quite much when compared to Donohos method. -
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