  Image smoothing represents a fundamental component of many disparate computer
vision and graphics applications. In this paper, we present a unified
unsupervised (label-free) learning framework that facilitates generating
flexible and high-quality smoothing effects by directly learning from data
using deep convolutional neural networks (CNNs). The heart of the design is the
training signal as a novel energy function that includes an edge-preserving
regularizer which helps maintain important yet potentially vulnerable image
structures, and a spatially-adaptive Lp flattening criterion which imposes
different forms of regularization onto different image regions for better
smoothing quality. We implement a diverse set of image smoothing solutions
employing the unified framework targeting various applications such as, image
abstraction, pencil sketching, detail enhancement, texture removal and
content-aware image manipulation, and obtain results comparable with or better
than previous methods. Moreover, our method is extremely fast with a modern GPU
(e.g, 200 fps for 1280x720 images). Our codes and model are released in
https://github.com/fqnchina/ImageSmoothing.
