论文标题
伽马米纳马克斯小波缩小了三分方先验
Gamma-Minimax Wavelet Shrinkage with Three-Point Priors
论文作者
论文摘要
在本文中，当可以提供有关信号的$ l^2 $ - 能量的事先信息时，我们提出了一种小波降级的小波降解信号。假设独立模型，根据该模型单独处理小波系数，我们提出了一个简单，依赖级别的收缩规则 $γ$ -Minimax适用于合适的先验。 当信噪比较低时，所提出的方法特别适合于降级任务，这是通过对标准测试功能的电池进行的模拟来说明的。提供了一些标准使用的小波收缩方法的比较。
In this paper we propose a method for wavelet denoising of signals contaminated with Gaussian noise when prior information about the $L^2$-energy of the signal is available. Assuming the independence model, according to which the wavelet coefficients are treated individually, we propose a simple, level dependent shrinkage rules that turn out to be $Γ$-minimax for a suitable class of priors. The proposed methodology is particularly well suited in denoising tasks when the signal-to-noise ratio is low, which is illustrated by simulations on the battery of standard test functions. Comparison to some standardly used wavelet shrinkage methods is provided.
