{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:09:43Z","timestamp":1750219783313,"version":"3.41.0"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T00:00:00Z","timestamp":1690329600000},"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":[[2023,8]]},"abstract":"<jats:p>Long exposure photography produces stunning imagery, representing moving elements in a scene with motion-blur. It is generally employed in two modalities, producing either a foreground or a background blur effect. Foreground blur images are traditionally captured on a tripod-mounted camera and portray blurred moving foreground elements, such as silky water or light trails, over a perfectly sharp background landscape. Background blur images, also called panning photography, are captured while the camera is tracking a moving subject, to produce an image of a sharp subject over a background blurred by relative motion. Both techniques are notoriously challenging and require additional equipment and advanced skills. In this paper, we describe a computational burst photography system that operates in a hand-held smartphone camera app, and achieves these effects fully automatically, at the tap of the shutter button. Our approach first detects and segments the salient subject. We track the scene motion over multiple frames and align the images in order to preserve desired sharpness and to produce aesthetically pleasing motion streaks. We capture an under-exposed burst and select the subset of input frames that will produce blur trails of controlled length, regardless of scene or camera motion velocity. We predict inter-frame motion and synthesize motion-blur to fill the temporal gaps between the input frames. Finally, we composite the blurred image with the sharp regular exposure to protect the sharpness of faces or areas of the scene that are barely moving, and produce a final high resolution and high dynamic range (HDR) photograph. Our system democratizes a capability previously reserved to professionals, and makes this creative style accessible to most casual photographers.<\/jats:p>","DOI":"10.1145\/3592124","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T14:29:21Z","timestamp":1690381761000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Computational Long Exposure Mobile Photography"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0498-6575","authenticated-orcid":false,"given":"Eric","family":"Tabellion","sequence":"first","affiliation":[{"name":"Google Research, Mountain View, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4935-7142","authenticated-orcid":false,"given":"Nikhil","family":"Karnad","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8865-4590","authenticated-orcid":false,"given":"Noa","family":"Glaser","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0395-0242","authenticated-orcid":false,"given":"Ben","family":"Weiss","sequence":"additional","affiliation":[{"name":"Google Research, Playa Vista, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8710-6065","authenticated-orcid":false,"given":"David E.","family":"Jacobs","sequence":"additional","affiliation":[{"name":"Google Research, Mountain View, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5419-3915","authenticated-orcid":false,"given":"Yael","family":"Pritch","sequence":"additional","affiliation":[{"name":"Google Research, Tel Aviv, Israel"}]}],"member":"320","published-online":{"date-parts":[[2023,7,26]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01923"},{"key":"e_1_2_2_2_1","unstructured":"Sameer Agarwal Keir Mierle and The Ceres Solver Team. 2022. Ceres Solver. Google Inc. https:\/\/github.com\/ceres-solver\/ceres-solver"},{"key":"e_1_2_2_3_1","volume-title":"BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs. ArXiv abs\/1907.05047","author":"Bazarevsky Valentin","year":"2019","unstructured":"Valentin Bazarevsky, Yury Kartynnik, Andrey Vakunov, Karthik Raveendran, and Matthias Grundmann. 2019. BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs. ArXiv abs\/1907.05047 (2019)."},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00700"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383325"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","unstructured":"Vincent Dumoulin and Francesco Visin. 2016. A guide to convolution arithmetic for deep learning. 10.48550\/ARXIV.1603.07285","DOI":"10.48550\/ARXIV.1603.07285"},{"key":"e_1_2_2_7_1","doi-asserted-by":"crossref","unstructured":"David S Ebert F Kenton Musgrave Darwyn Peachey Ken Perlin and Steven Worley. 2003. Texturing & modeling: a procedural approach. Morgan Kaufmann.","DOI":"10.1016\/B978-155860848-1\/50029-2"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995525"},{"key":"e_1_2_2_9_1","unstructured":"Monika Gupta. 2021. Google Tensor is a milestone for machine learning. https:\/\/blog.google\/products\/pixel\/introducing-google-tensor"},{"key":"e_1_2_2_10_1","doi-asserted-by":"crossref","unstructured":"R. I. Hartley and A. Zisserman. 2004. Multiple View Geometry in Computer Vision (second ed.). Cambridge University Press ISBN: 0521540518.","DOI":"10.1017\/CBO9780511811685"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2980254"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.213"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177703732"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298710"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13646"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2009.01498.x"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2010324.1964950"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356508"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1531326.1531350"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1899404.1899408"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661272"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2461995"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.20380\/GI2018.15"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2889485"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506443"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.1998.678102"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2010.01840.x"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00003"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2004.1394427"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20071-7_15"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2021.01.006"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13870"},{"volume-title":"Fast Alternatives to Perlin's Bias and Gain Functions","author":"Schlick Christophe","key":"e_1_2_2_36_1","unstructured":"Christophe Schlick. 1994. Fast Alternatives to Perlin's Bias and Gain Functions. Academic Press Professional, Inc., USA, 401--403."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8803679"},{"key":"e_1_2_2_38_1","unstructured":"Spectre. [n. d.]. Spectre app. https:\/\/spectre.cam. Accessed: 2023-01-17."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2014.2377753"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-009-0405-6"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201329"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141918"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323024"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.303"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.2991\/cnct-16.2017.44"},{"key":"e_1_2_2_46_1","volume-title":"International conference on pattern recognition and image processing. 116--119","author":"Zin\u00dfer Timo","year":"2005","unstructured":"Timo Zin\u00dfer, Jochen Schmidt, and Heinrich Niemann. 2005. Point set registration with integrated scale estimation. In International conference on pattern recognition and image processing. 116--119."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592124","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3592124","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:46Z","timestamp":1750178266000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3592124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,26]]},"references-count":45,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.1145\/3592124"],"URL":"https:\/\/doi.org\/10.1145\/3592124","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"type":"print","value":"0730-0301"},{"type":"electronic","value":"1557-7368"}],"subject":[],"published":{"date-parts":[[2023,7,26]]},"assertion":[{"value":"2023-07-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}