  Most deep image smoothing operators are always trained repetitively when
different explicit structure-texture pairs are employed as label images for
each algorithm configured with different parameters. This kind of training
strategy often takes a long time and spends equipment resources in a costly
manner. To address this challenging issue, we generalize continuous network
interpolation as a more powerful model generation tool, and then propose a
simple yet effective model generation strategy to form a sequence of models
that only requires a set of specific-effect label images. To precisely learn
image smoothing operators, we present a double-state aggregation (DSA) module,
which can be easily inserted into most of current network architecture. Based
on this module, we design a double-state aggregation neural network structure
with a local feature aggregation block and a nonlocal feature aggregation block
to obtain operators with large expression capacity. Through the evaluation of
many objective and visual experimental results, we show that the proposed
method is capable of producing a series of continuous models and achieves
better performance than that of several state-of-the-art methods for image
smoothing.
