期刊
ACM TRANSACTIONS ON GRAPHICS
卷 37, 期 6, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3272127.3275081
关键词
image smoothing; edge preservation; unsupervised learning
资金
- National 973 Program [2015CB352501]
- NSFC-ISF [61561146397]
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 L-p 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).
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