  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.
