{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T02:55:35Z","timestamp":1773975335209,"version":"3.50.1"},"reference-count":54,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,4,30]],"date-time":"2018-04-30T00:00:00Z","timestamp":1525046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2018,4,30]]},"abstract":"<jats:p>\n            Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from\n            <jats:italic>paired<\/jats:italic>\n            training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this article a deep learning approach that is instead trained on\n            <jats:italic>unpaired<\/jats:italic>\n            data, namely, a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits rather than just a \u201cblack-box\u201d result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.\n          <\/jats:p>","DOI":"10.1145\/3181974","type":"journal-article","created":{"date-parts":[[2018,5,14]],"date-time":"2018-05-14T12:29:08Z","timestamp":1526300948000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":235,"title":["Exposure"],"prefix":"10.1145","volume":"37","author":[{"given":"Yuanming","family":"Hu","sequence":"first","affiliation":[{"name":"Microsoft Research 8 MIT CSAIL"}]},{"given":"Hao","family":"He","sequence":"additional","affiliation":[{"name":"Microsoft Research 8 MIT CSAIL"}]},{"given":"Chenxi","family":"Xu","sequence":"additional","affiliation":[{"name":"Microsoft Research 8 Peking University"}]},{"given":"Baoyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Microsoft Research"}]},{"given":"Stephen","family":"Lin","sequence":"additional","affiliation":[{"name":"Microsoft Research"}]}],"member":"320","published-online":{"date-parts":[[2018,5,12]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. 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Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Man\u00e9, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Vi\u00e9gas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved January 2017 from http:\/\/tensorflow.org\/."},{"key":"e_1_2_2_2_1","unstructured":"Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein GAN. arXiv:1701.07875.  Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. 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In Proceedings of the IEEE International Conference on Computer Vision (ICCV\u201917)."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3181974","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3181974","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T21:37:50Z","timestamp":1750282670000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3181974"}},"subtitle":["A White-Box Photo Post-Processing Framework"],"short-title":[],"issued":{"date-parts":[[2018,4,30]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,4,30]]}},"alternative-id":["10.1145\/3181974"],"URL":"https:\/\/doi.org\/10.1145\/3181974","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,30]]},"assertion":[{"value":"2017-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-05-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}