  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
