Image denoising using scale mixtures of Gaussians in the wavelet domain
Image denoising using scale mixtures of Gaussians in the wavelet domain
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DOI：
10.1109/tip.2003.818640
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发表时间：
2003-11-01
影响因子：
10.6
通讯作者：
Simoncelli, EP
中科院分区：
文献类型：
--
作者：
Portilla, J;Strela, V;Simoncelli, EP
我们描述了一种方法，用于从数字图像中去除噪声，基于一个统计模型的系数过完整的多尺度导向的基础上。相邻位置和尺度的系数邻域被建模为两个独立随机变量的乘积：高斯向量和隐藏的正标量乘子。后者调制的系数在附近的局部方差，因此能够占经验观察到的系数幅度之间的相关性。在该模型下，每个系数的贝叶斯最小二乘估计值简化为隐藏乘数变量的所有可能值的局部线性估计值的加权平均值。我们证明，通过模拟与图像污染的加性白色高斯噪声，该方法的性能大大超过以前公布的方法，无论是在视觉上和均方误差。
We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an over-complete 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.