Abstract Many different deep networks have been used to approximate,
accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these traditional operators,
many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as “parameterized image operators”.
However, most existing deep networks trained for these operators are
only designed for one specific parameter configuration, which does not
meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decouple
learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted
as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end
jointly trained with the base network. Experiments demonstrate that
the proposed framework can be successfully applied to many traditional
parameterized image operators. We provide more analysis to better understand the proposed framework, which may inspire more promising
research in this direction. Our codes and models have been released in
https://github.com/fqnchina/DecoupleLearning.