{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T13:34:50Z","timestamp":1777988090919,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,24]],"date-time":"2021-07-24T00:00:00Z","timestamp":1627084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["N.A."],"award-info":[{"award-number":["N.A."]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral imaging (HSI) provides additional information compared to regular color imaging, making it valuable in areas such as biomedicine, materials inspection and food safety. However, HSI is challenging because of the large amount of data and long measurement times involved. Compressed sensing (CS) approaches to HSI address this, albeit subject to tradeoffs between image reconstruction accuracy, time and generalizability to different types of scenes. Here, we develop improved CS approaches for HSI, based on parallelized multitrack acquisition of multiple spectra per shot. The multitrack architecture can be paired up with either of the two compatible CS algorithms developed here: (1) a sparse recovery algorithm based on block compressed sensing and (2) an adaptive CS algorithm based on sampling in the wavelet domain. As a result, the measurement speed can be drastically increased while maintaining reconstruction speed and accuracy. The methods were validated computationally both in noiseless as well as noisy simulated measurements. Multitrack adaptive CS has a \u223c10 times shorter measurement plus reconstruction time as compared to full sampling HSI without compromising reconstruction accuracy across the sample images tested. Multitrack non-adaptive CS (sparse recovery) is most robust against Poisson noise at the expense of longer reconstruction times.<\/jats:p>","DOI":"10.3390\/s21155034","type":"journal-article","created":{"date-parts":[[2021,7,25]],"date-time":"2021-07-25T22:07:00Z","timestamp":1627250820000},"page":"5034","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multitrack Compressed Sensing for Faster Hyperspectral Imaging"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4060-9358","authenticated-orcid":false,"given":"Sharvaj","family":"Kubal","sequence":"first","affiliation":[{"name":"Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore"},{"name":"Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology Centre, 1 Create Way, Singapore 138602, Singapore"},{"name":"School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8604-8707","authenticated-orcid":false,"given":"Elizabeth","family":"Lee","sequence":"additional","affiliation":[{"name":"Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology Centre, 1 Create Way, Singapore 138602, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6721-7106","authenticated-orcid":false,"given":"Chor Yong","family":"Tay","sequence":"additional","affiliation":[{"name":"School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore"},{"name":"School of Biological Sciences, Nanyang Technological University, 50 Nanyang Ave., Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6806-8472","authenticated-orcid":false,"given":"Derrick","family":"Yong","sequence":"additional","affiliation":[{"name":"Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, 2 Fusionopolis Way, Singapore 138634, Singapore"},{"name":"Critical Analytics for Manufacturing Personalized-Medicine, Singapore-MIT Alliance for Research and Technology Centre, 1 Create Way, Singapore 138602, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"010901","DOI":"10.1117\/1.JBO.19.1.010901","article-title":"Medical hyperspectral imaging: A review","volume":"19","author":"Lu","year":"2014","journal-title":"J. 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