Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
This work addresses image denoising for applications requiring transparency and performance, though it is incremental as it builds on classical wavelet shrinkage methods.
The paper tackled the problem of bridging the gap between data-driven and model-driven image denoising by introducing a generic adaptive wavelet shrinkage function that adapts to wavelet scales and noise standard deviation, resulting in significant performance improvements over classical shrinkage functions.
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.