  Many different deep networks have been used to approximate, accelerate or
improve traditional image operators. 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 decoupled 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. To accelerate the parameter tuning for practical
scenarios, the proposed framework can be further extended to dynamically change
the weights of only one single layer of the base network while sharing most
computation cost. We demonstrate that this cheap parameter-tuning extension of
the proposed decoupled learning framework even outperforms the state-of-the-art
alternative approaches.
