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Most deep image smoothing operators are always trained repetitively whendifferent explicit structure-texture pairs are employed as label images foreach algorithm configured with different parameters. This kind of trainingstrategy often takes a long time and spends equipment resources in a costlymanner. To address this challenging issue, we generalize continuous networkinterpolation as a more powerful model generation tool, and then propose asimple yet effective model generation strategy to form a sequence of modelsthat only requires a set of specific-effect label images. To precisely learnimage smoothing operators, we present a double-state aggregation (DSA) module,which can be easily inserted into most of current network architecture. Basedon this module, we design a double-state aggregation neural network structurewith a local feature aggregation block and a nonlocal feature aggregation blockto obtain operators with large expression capacity. Through the evaluation ofmany objective and visual experimental results, we show that the proposedmethod is capable of producing a series of continuous models and achievesbetter performance than that of several state-of-the-art methods for imagesmoothing.

Concurrently Extrapolating and Interpolating Networks for Continuous Model Generation