一種小波系數(shù)模型在圖像噪聲參數(shù)估計中的應(yīng)用
On the Usage of a Wavelet Coefficient Model in Noise Variance Estimation of Image
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摘要: 在小波圖像處理中，通常利用HH子帶來估計高斯白噪聲方差，目前流行的估計方法是由Donoho和Johnstone提出的(簡稱DJ法)，但是該方法給出的估計值通常都偏大。針對這一點(diǎn)，該文將他們的方法結(jié)合雙隨機(jī)小波系數(shù)模型，提出了一種新的、遞歸的方差估計方法。在已由Donoho的方法獲得噪聲方差估計的粗略估計的情況下，新方法利用統(tǒng)計學(xué)理論將HH子帶中的信號濾除從而得到更接近于純噪聲的HH子帶，然后利用這一新的HH子帶來估計噪聲的方差。結(jié)合EM參數(shù)估計方法，該方法還可以實(shí)現(xiàn)非高斯噪聲參數(shù)的估計，實(shí)驗(yàn)表明新方法同Donoho法相比有很大的改善。
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關(guān)鍵詞:
- 系數(shù)模型; EM算法; 小波變換
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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