  The rise of machine learning in image processing has created a gap between
trainable data-driven and classical model-driven approaches: While
learning-based models often show superior performance, classical ones are often
more transparent. To reduce this gap, we introduce a generic wavelet shrinkage
function for denoising which is adaptive to both the wavelet scales as well as
the noise standard deviation. It is inferred from trained results of a tightly
parametrised function which is inherited from nonlinear diffusion. Our proposed
shrinkage function is smooth and compact while only using two parameters. In
contrast to many existing shrinkage functions, it is able to enhance image
structures by amplifying wavelet coefficients. Experiments show that it
outperforms classical shrinkage functions by a significant margin.
