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 $\Gamma$-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.

翻译：在本文中,当事先掌握关于信号的2美元能量的信息时,我们建议采用一种方法来消除受高斯噪音污染的信号的波浪;假设独立模式,根据这种独立模式单独处理波浪系数,我们建议采用一个简单、水平的依附缩水规则,该规则对适当的前科类别来说是$\Gamma$-最小值;在信号对噪音比率低时,拟议方法特别适合去注工作,通过对标准测试功能电池的模拟加以说明;提供与某些标准使用的波浪缩水方法的比较。