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Graph."],"published-print":{"date-parts":[[2016,5,25]]},"abstract":"<jats:p>Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work, but such an algorithm faces many hurdles. Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics. Further, these adjustments are often spatially varying. Existing automatic algorithms are still limited and cover only a subset of these challenges. Recently, deep learning has shown unique abilities to address hard problems. This motivated us to explore the use of deep neural networks (DNNs) in the context of photo editing. In this article, we formulate automatic photo adjustment in a manner suitable for this approach. We also introduce an image descriptor accounting for the local semantics of an image. Our experiments demonstrate that training DNNs using these descriptors successfully capture sophisticated photographic styles. In particular and unlike previous techniques, it can model local adjustments that depend on image semantics. We show that this yields results that are qualitatively and quantitatively better than previous work.<\/jats:p>","DOI":"10.1145\/2790296","type":"journal-article","created":{"date-parts":[[2016,2,22]],"date-time":"2016-02-22T08:07:16Z","timestamp":1456128436000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":208,"title":["Automatic Photo Adjustment Using Deep Neural Networks"],"prefix":"10.1145","volume":"35","author":[{"given":"Zhicheng","family":"Yan","sequence":"first","affiliation":[{"name":"University of Illinois at Urbana Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvain","family":"Paris","sequence":"additional","affiliation":[{"name":"Adobe Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhou","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Hong Kong and University of Illinois at Urbana Champaign, Pokfulam Road, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2016,2,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1360612.1360639"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141935"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.993558"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995413"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995439"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141933"},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. 2217--2224","author":"Dale K.","unstructured":"K. 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