数学
模式识别(心理学)
小波
高斯分布
高斯噪声
标量(数学)
均方误差
加性高斯白噪声
贝叶斯概率
白噪声
人工智能
统计
算法
计算机科学
物理
量子力学
几何学
作者
Javier Portilla,Vasily Strela,Martin J. Wainwright,Eero P. Simoncelli
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2003-11-01
卷期号:12 (11): 1338-1351
被引量:2155
标识
DOI:10.1109/tip.2003.818640
摘要
We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error.
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