{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T15:18:29Z","timestamp":1793114309684,"version":"4.5.0"},"reference-count":47,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,7,20]],"date-time":"2017-07-20T00:00:00Z","timestamp":1500508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100004405","name":"Toyota","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004405","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2017,8,31]]},"abstract":"<jats:p>\n            Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input\/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new\n            <jats:italic>slicing<\/jats:italic>\n            node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.\n          <\/jats:p>","DOI":"10.1145\/3072959.3073592","type":"journal-article","created":{"date-parts":[[2017,7,21]],"date-time":"2017-07-21T12:24:07Z","timestamp":1500639847000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":718,"title":["Deep bilateral learning for real-time image enhancement"],"prefix":"10.1145","volume":"36","author":[{"given":"Micha\u00ebl","family":"Gharbi","sequence":"first","affiliation":[{"name":"MIT CSAIL"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Chen","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan T.","family":"Barron","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel W.","family":"Hasinoff","sequence":"additional","affiliation":[{"name":"Google Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fr\u00e9do","family":"Durand","sequence":"additional","affiliation":[{"name":"Universit\u00e9 C\u00f4te d'Azur"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,7,20]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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. (2015). http:\/\/tensorflow.org\/  Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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. (2015). http:\/\/tensorflow.org\/"},{"key":"e_1_2_2_2_1","volume-title":"Fast High-Dimensional Filtering Using the Permutohedral Lattice. Computer Graphics Forum","author":"Adams Andrew","year":"2010","unstructured":"Andrew Adams , Jongmin Baek , and Myers Abraham Davis . 2010. Fast High-Dimensional Filtering Using the Permutohedral Lattice. Computer Graphics Forum ( 2010 ). Andrew Adams, Jongmin Baek, and Myers Abraham Davis. 2010. Fast High-Dimensional Filtering Using the Permutohedral Lattice. Computer Graphics Forum (2010)."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629645"},{"key":"e_1_2_2_4_1","volume-title":"Fast bilateral-space stereo for synthetic defocus. CVPR","author":"Barron Jonathan T","year":"2015","unstructured":"Jonathan T Barron , Andrew Adams , YiChang Shih , and Carlos Hern\u00e1ndez . 2015. Fast bilateral-space stereo for synthetic defocus. CVPR ( 2015 ). Jonathan T Barron, Andrew Adams, YiChang Shih, and Carlos Hern\u00e1ndez. 2015. Fast bilateral-space stereo for synthetic defocus. CVPR (2015)."},{"key":"e_1_2_2_5_1","volume-title":"The Fast Bilateral Solver. ECCV","author":"Barron Jonathan T","year":"2016","unstructured":"Jonathan T Barron and Ben Poole . 2016. The Fast Bilateral Solver. ECCV ( 2016 ). Jonathan T Barron and Ben Poole. 2016. The Fast Bilateral Solver. ECCV (2016)."},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1661412.1618476"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995413"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2982423"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276506"},{"key":"e_1_2_2_10_1","volume-title":"Kaiming He, and Xiaoou Tang.","author":"Dong Chao","year":"2014","unstructured":"Chao Dong , Chen Change Loy , Kaiming He, and Xiaoou Tang. 2014 . Learning a deep convolutional network for image super-resolution. ECCV ( 2014). Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2014. Learning a deep convolutional network for image super-resolution. ECCV (2014)."},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969091"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2070781.2024209"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2982399"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818127"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980254"},{"key":"e_1_2_2_16_1","volume-title":"Fast Guided Filter. CoRR","author":"He Kaiming","year":"2015","unstructured":"Kaiming He and Jian Sun . 2015. Fast Guided Filter. CoRR ( 2015 ). Kaiming He and Jian Sun. 2015. Fast Guided Filter. CoRR (2015)."},{"key":"e_1_2_2_17_1","volume-title":"Guided image filtering. TPAMI","author":"He Kaiming","year":"2013","unstructured":"Kaiming He , Jian Sun , and Xiaoou Tang . 2013. Guided image filtering. TPAMI ( 2013 ). Kaiming He, Jian Sun, and Xiaoou Tang. 2013. Guided image filtering. TPAMI (2013)."},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601174"},{"key":"e_1_2_2_20_1","volume-title":"Context-based automatic local image enhancement. ECCV","author":"Hwang Sung Ju","year":"2012","unstructured":"Sung Ju Hwang , Ashish Kapoor , and Sing Bing Kang . 2012. Context-based automatic local image enhancement. ECCV ( 2012 ). Sung Ju Hwang, Ashish Kapoor, and Sing Bing Kang. 2012. Context-based automatic local image enhancement. ECCV (2012)."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925974"},{"key":"e_1_2_2_22_1","volume-title":"Evolution of optical flow estimation with deep networks. CoRR","author":"Ilg Eddy","year":"2016","unstructured":"Eddy Ilg , Nikolaus Mayer , Tonmoy Saikia , Margret Keuper , Alexey Dosovitskiy , and Thomas Brox . 2016. Flownet 2.0 : Evolution of optical flow estimation with deep networks. CoRR ( 2016 ). Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. 2016. Flownet 2.0: Evolution of optical flow estimation with deep networks. CoRR (2016)."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"e_1_2_2_24_1","volume-title":"Image-to-Image Translation with Conditional Adversarial Networks. CoRR","author":"Isola Phillip","year":"2016","unstructured":"Phillip Isola , Jun-Yan Zhu , Tinghui Zhou , and Alexei A Efros . 2016. Image-to-Image Translation with Conditional Adversarial Networks. CoRR ( 2016 ). Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2016. Image-to-Image Translation with Conditional Adversarial Networks. CoRR (2016)."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969442.2969465"},{"key":"e_1_2_2_26_1","volume-title":"FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report UM-CS-2010--009","author":"Jain Vidit","year":"2010","unstructured":"Vidit Jain and Erik Learned-Miller . 2010 . FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report UM-CS-2010--009 . University of Massachusetts , Amherst. Vidit Jain and Erik Learned-Miller. 2010. FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report UM-CS-2010--009. University of Massachusetts, Amherst."},{"key":"e_1_2_2_27_1","volume-title":"Gehler","author":"Jampani Varun","year":"2016","unstructured":"Varun Jampani , Martin Kiefel , and Peter V . Gehler . 2016 . Learning Sparse High Dimensional Filters: Image Filtering , Dense CRFs and Bilateral Neural Networks. CVPR ( 2016). Varun Jampani, Martin Kiefel, and Peter V. Gehler. 2016. Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks. CVPR (2016)."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2012.03225.x"},{"key":"e_1_2_2_29_1","volume-title":"Adam: A method for stochastic optimization. ICLR","author":"Kingma Diederik","year":"2015","unstructured":"Diederik Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization. ICLR (2015). Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. ICLR (2015)."},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276497"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1177"},{"key":"e_1_2_2_33_1","volume-title":"Learning recursive filters for low-level vision via a hybrid neural network. ECCV","author":"Liu Sifei","year":"2016","unstructured":"Sifei Liu , Jinshan Pan , and Ming-Hsuan Yang . 2016. Learning recursive filters for low-level vision via a hybrid neural network. ECCV ( 2016 ). Sifei Liu, Jinshan Pan, and Ming-Hsuan Yang. 2016. Learning recursive filters for low-level vision via a hybrid neural network. ECCV (2016)."},{"key":"e_1_2_2_34_1","volume-title":"Fully convolutional networks for semantic segmentation. CVPR","author":"Long Jonathan","year":"2015","unstructured":"Jonathan Long , Evan Shelhamer , and Trevor Darrell . 2015. Fully convolutional networks for semantic segmentation. CVPR ( 2015 ). Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. CVPR (2015)."},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925952"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/11744085_44"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964963"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185528"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_2_40_1","volume-title":"Deep Automatic Portrait Matting. ECCV","author":"Shen Xiaoyong","year":"2016","unstructured":"Xiaoyong Shen , Xin Tao , Hongyun Gao , Chao Zhou , and Jiaya Jia . 2016. Deep Automatic Portrait Matting. ECCV ( 2016 ). Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. 2016. Deep Automatic Portrait Matting. ECCV (2016)."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508419"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/938978.939190"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045296"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2790296"},{"key":"e_1_2_2_45_1","volume-title":"Multi-scale context aggregation by dilated convolutions. CoRR","author":"Yu Fisher","year":"2015","unstructured":"Fisher Yu and Vladlen Koltun . 2015. Multi-scale context aggregation by dilated convolutions. CoRR ( 2015 ). Fisher Yu and Vladlen Koltun. 2015. Multi-scale context aggregation by dilated convolutions. CoRR (2015)."},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126492"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3072959.3073592","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3072959.3073592","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:37:22Z","timestamp":1750217842000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3072959.3073592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,20]]},"references-count":47,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,8,31]]}},"alternative-id":["10.1145\/3072959.3073592"],"URL":"https:\/\/doi.org\/10.1145\/3072959.3073592","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,20]]},"assertion":[{"value":"2017-07-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}