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Graph."],"published-print":{"date-parts":[[2018,12,31]]},"abstract":"<jats:p>\n            Once a color image is converted to grayscale, it is a common belief that the original color cannot be fully restored, even with the state-of-the-art colorization methods. In this paper, we propose an innovative method to synthesize\n            <jats:italic>invertible grayscale.<\/jats:italic>\n            It is a grayscale image that can fully restore its original color. The key idea here is to encode the original color information into the synthesized grayscale, in a way that users cannot recognize any anomalies. We propose to learn and embed the color-encoding scheme via a convolutional neural network (CNN). It consists of an encoding network to convert a color image to grayscale, and a decoding network to invert the grayscale to color. We then design a loss function to ensure the trained network possesses three required properties: (a) color invertibility, (b) grayscale conformity, and (c) resistance to quantization error. We have conducted intensive quantitative experiments and user studies over a large amount of color images to validate the proposed method. Regardless of the genre and content of the color input, convincing results are obtained in all cases.\n          <\/jats:p>","DOI":"10.1145\/3272127.3275080","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T19:16:10Z","timestamp":1543432570000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Invertible grayscale"],"prefix":"10.1145","volume":"37","author":[{"given":"Menghan","family":"Xia","sequence":"first","affiliation":[{"name":"SIAT, China and The Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueting","family":"Liu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tien-Tsin","family":"Wong","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong and Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, SIAT, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_2_1_1","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume":"16","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . 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