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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\n            <jats:italic>\n              L\n              <jats:sub>p<\/jats:sub>\n            <\/jats:italic>\n            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 1280\u00d7720 images).\n          <\/jats:p>","DOI":"10.1145\/3272127.3275081","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T19:16:10Z","timestamp":1543432570000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":96,"title":["Image smoothing via unsupervised learning"],"prefix":"10.1145","volume":"37","author":[{"given":"Qingnan","family":"Fan","sequence":"first","affiliation":[{"name":"Shandong University"}]},{"given":"Jiaolong","family":"Yang","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]},{"given":"David","family":"Wipf","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]},{"given":"Baoquan","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, Shandong University"}]},{"given":"Xin","family":"Tong","sequence":"additional","affiliation":[{"name":"Microsoft Research Asia"}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-STS394"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2291328"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766946"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.126"},{"key":"e_1_2_1_5_1","first-page":"4","article-title":"Stylebank: An explicit representation for neural image style transfer","volume":"1","author":"Chen Dongdong","year":"2017","unstructured":"Dongdong Chen , Lu Yuan , Jing Liao , Nenghai Yu , and Gang Hua . 2017 b. 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