Image Smoothing via Unsupervised Learning
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.
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Qingnan Fan (add twitter)
Jiaolong Yang (add twitter)
David Wipf (edit)
Baoquan Chen (add twitter)
Xin Tong (add twitter)
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Implementation codes of SIGGRAPH Asia 2018 paper "Image Smoothing via Unsupervised Learning"
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11/07/18 06:03PM
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arxivml: "Image Smoothing via Unsupervised Learning", Qingnan Fan, Jiaolong Yang, David Wipf, Baoquan Chen, Xin Tong https://t.co/vXwL9HR0yv
arxiv_cscv: Image Smoothing via Unsupervised Learning https://t.co/aYDSCZDM5k
nmfeeds: [O] https://t.co/ReGg0S9cnT Image Smoothing via Unsupervised Learning. Image smoothing represents a fundamental component ...
nmfeeds: [CV] https://t.co/ReGg0S9cnT Image Smoothing via Unsupervised Learning. Image smoothing represents a fundamental component...
arxiv_cscv: Image Smoothing via Unsupervised Learning https://t.co/aYDSCZDM5k
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