Xie Jie-cheng, Zhang Da-li, Xu Wen-li. On the Usage of a Wavelet Coefficient Model in Noise Variance Estimation of Image[J]. Journal of Electronics & Information Technology, 2004, 26(5): 673-678.
Citation:
Xie Jie-cheng, Zhang Da-li, Xu Wen-li. On the Usage of a Wavelet Coefficient Model in Noise Variance Estimation of Image[J]. Journal of Electronics & Information Technology, 2004, 26(5): 673-678.
Xie Jie-cheng, Zhang Da-li, Xu Wen-li. On the Usage of a Wavelet Coefficient Model in Noise Variance Estimation of Image[J]. Journal of Electronics & Information Technology, 2004, 26(5): 673-678.
Citation:
Xie Jie-cheng, Zhang Da-li, Xu Wen-li. On the Usage of a Wavelet Coefficient Model in Noise Variance Estimation of Image[J]. Journal of Electronics & Information Technology, 2004, 26(5): 673-678.
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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