Pizurica Aleksandra, Philips Wilfried
Department for Telecommunications and Information Processing (TELIN), Ghent University, B-9000 Ghent, Belgium.
IEEE Trans Image Process. 2006 Mar;15(3):654-65. doi: 10.1109/tip.2005.863698.
We develop three novel wavelet domain denoising methods for subband-adaptive, spatially-adaptive and multivalued image denoising. The core of our approach is the estimation of the probability that a given coefficient contains a significant noise-free component, which we call "signal of interest." In this respect, we analyze cases where the probability of signal presence is 1) fixed per subband, 2) conditioned on a local spatial context, and 3) conditioned on information from multiple image bands. All the probabilities are estimated assuming a generalized Laplacian prior for noise-free subband data and additive white Gaussian noise. The results demonstrate that the new subband-adaptive shrinkage function outperforms Bayesian thresholding approaches in terms of mean-squared error. The spatially adaptive version of the proposed method yields better results than the existing spatially adaptive ones of similar and higher complexity. The performance on color and on multispectral images is superior with respect to recent multiband wavelet thresholding.
我们开发了三种新颖的小波域去噪方法，用于子带自适应、空间自适应和多值图像去噪。我们方法的核心是估计给定系数包含显著无噪声成分（我们称之为“感兴趣信号”）的概率。在这方面，我们分析了信号存在概率为以下三种情况：1）每个子带固定；2）基于局部空间上下文；3）基于多个图像波段的信息。所有概率的估计均假设无噪声子带数据的广义拉普拉斯先验和加性高斯白噪声。结果表明，新的子带自适应收缩函数在均方误差方面优于贝叶斯阈值处理方法。所提方法的空间自适应版本比现有复杂度相似或更高的空间自适应方法产生更好的结果。在彩色和多光谱图像上的性能相对于最近的多波段小波阈值处理更优。
