Hammond David K, Simoncelli Eero P
Ecole Polytechnique Federale de Lausanne, 1015 Lausanne, Switzerland.
IEEE Trans Image Process. 2008 Nov;17(11):2089-101. doi: 10.1109/tip.2008.2004796.
We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated according to the values of two hidden random variables, thus allowing the model to adapt to the local amplitude and orientation of the signal. A third hidden variable selects between this oriented process and a nonoriented scale mixture of Gaussians process, thus providing adaptability to the local orientedness of the signal. Based on this model, we develop an optimal Bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with Gaussian scale mixtures.
我们开发了一种统计模型,用于描述多尺度图像表示中系数局部邻域的空间变化行为。邻域被建模为多元高斯密度的样本,这些样本根据两个隐藏随机变量的值进行调制和旋转,从而使模型能够适应信号的局部幅度和方向。第三个隐藏变量在这个有向过程和高斯过程的无向尺度混合之间进行选择,从而提供对信号局部方向性的适应性。基于该模型,我们开发了一种用于图像去噪的最优贝叶斯最小二乘估计器,并通过模拟表明,所得方法相对于先前发表的使用高斯尺度混合获得的结果有显著改进。