Lyu Siwei, Simoncelli Eero P
Computer Science Department, University at Albany, State University of New York, Albany, NY 12222, USA.
IEEE Trans Pattern Anal Mach Intell. 2009 Apr;31(4):693-706. doi: 10.1109/TPAMI.2008.107.
The local statistical properties of photographic images, when represented in a multi-scale basis, have been described using Gaussian scale mixtures. Here, we use this local description as a substrate for constructing a global field of Gaussian scale mixtures (FoGSMs). Specifically, we model multi-scale subbands as a product of an exponentiated homogeneous Gaussian Markov random field (hGMRF) and a second independent hGMRF. We show that parameter estimation for this model is feasible, and that samples drawn from a FoGSM model have marginal and joint statistics similar to subband coefficients of photographic images. We develop an algorithm for removing additive Gaussian white noise based on the FoGSM model, and demonstrate denoising performance comparable with state-of-the-art methods.
当以多尺度为基础表示时,摄影图像的局部统计特性已使用高斯尺度混合来描述。在此,我们将这种局部描述用作构建高斯尺度混合全局场(FoGSMs)的基础。具体而言,我们将多尺度子带建模为指数化齐次高斯马尔可夫随机场(hGMRF)与第二个独立hGMRF的乘积。我们表明该模型的参数估计是可行的,并且从FoGSM模型抽取的样本具有与摄影图像子带系数相似的边际和联合统计特性。我们基于FoGSM模型开发了一种去除加性高斯白噪声的算法,并证明其去噪性能与现有最先进方法相当。