{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:48Z","timestamp":1750309548513,"version":"3.41.0"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"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":["GetMobile: Mobile Comp. and Comm."],"published-print":{"date-parts":[[2024,10,22]]},"abstract":"<jats:p>Sensing in low-light and dark environments has a wide range of applications. However, existing sensing technologies suffer several major challenges, such as excessive noise and low resolution. In this work, we propose Mozart - a new mobile sensing system that leverages off-the-shelf Time-of-Flight (ToF) depth cameras to generate high-resolution and rich-in-texture maps for applications in dark scenarios. The design of Mozart is based on our key observation that the phase components of ToF measurements can be manipulated to expose texture information. Through in-depth analysis of the physical reflection model, we show that the textures can be exposed and enhanced using highly compute-efficient phase manipulation functions. Moreover, by exploiting the physics texture models, we propose an autoencoderbased unsupervised learning approach that can automatically learn efficient representations from phase components to generate high-resolution maps. We implemented Mozart on several Android smartphone models and an edge testbed with standalone ToF camera platforms for various applications in the dark. The results show that Mozart can work in real time and delivers significant improvement over existing sensing technologies. The demo video at https:\/\/ www.youtube.com\/watch?v=L_sxyTZxIdU shows that Mozart offers a low-cost, highperformance sensing technology for nextgeneration applications in the dark.<\/jats:p>","DOI":"10.1145\/3701701.3701711","type":"journal-article","created":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T22:26:35Z","timestamp":1729635995000},"page":"30-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Mozart: A Mobile ToF System for Sensing in the Dark Through Phase Manipulation"],"prefix":"10.1145","volume":"28","author":[{"given":"Zhiyuan","family":"Xie","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomin","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Pan","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Xing","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Liu","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485927"},{"key":"e_1_2_1_2_1","volume-title":"Pedrotti","author":"Pedrotti Frank L.","year":"2017","unstructured":"Frank L. Pedrotti, Leno M. Pedrotti, and Leno S. Pedrotti. 2017. Introduction to optics. Cambridge University Press."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01102"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210315"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00482"},{"volume-title":"Under-display camera image enhancement via cascaded curve estimation","author":"Luo Jun","key":"e_1_2_1_8_1","unstructured":"Jun Luo, Wenqi Ren, Tao Wang, Chongyi Li, and Xiaochun Cao. 2022. Under-display camera image enhancement via cascaded curve estimation. IEEE Transactions on Image Processing 3; 4856--4868."},{"volume-title":"Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services; 163--176","author":"Xie Zhiyuan","key":"e_1_2_1_9_1","unstructured":"Zhiyuan Xie, Xiaomin Ouyang, Li Pan, Wenrui Lu, Guoliang Xing, and Xiaoming Liu. Mozart: A mobile ToF system for sensing in the dark through phase manipulation. 2023. Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services; 163--176."},{"key":"e_1_2_1_10_1","first-page":"3289","article-title":"Convolutional sparse autoencoders for image classification","volume":"29","author":"Luo Wei","year":"2017","unstructured":"Wei Luo, Jun Li, Jian Yang, Wei Xu, and Jian Zhang. 2017. Convolutional sparse autoencoders for image classification. IEEE Transactions on Neural Networks and Learning Systems; 29, 7, 3289--3294.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems;"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3384419.3430776"},{"key":"e_1_2_1_12_1","unstructured":"tegrastats Utility https:\/\/docs.nvidia.com\/drive\/ drive_os_5.1.6.1L\/nvvib_docs\/index.html#page\/ DRIVE_OS_Linux_SDK_Development_Guide\/ Utilities\/util_tegrastats.html"}],"container-title":["GetMobile: Mobile Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701701.3701711","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701701.3701711","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:44Z","timestamp":1750295924000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701701.3701711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"references-count":12,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,10,22]]}},"alternative-id":["10.1145\/3701701.3701711"],"URL":"https:\/\/doi.org\/10.1145\/3701701.3701711","relation":{},"ISSN":["2375-0529","2375-0537"],"issn-type":[{"type":"print","value":"2375-0529"},{"type":"electronic","value":"2375-0537"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"2024-10-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}