{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:47:00Z","timestamp":1773154020766,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2015,5,22]],"date-time":"2015-05-22T00:00:00Z","timestamp":1432252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.<\/jats:p>","DOI":"10.3390\/s150512053","type":"journal-article","created":{"date-parts":[[2015,5,26]],"date-time":"2015-05-26T04:16:36Z","timestamp":1432613796000},"page":"12053-12079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Wonseok","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea"}]},{"given":"Soohwan","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea"}]},{"given":"Seungyong","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8593-7155","authenticated-orcid":false,"given":"Joonki","family":"Paik","sequence":"additional","affiliation":[{"name":"Department of Image, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2015,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.sigpro.2009.05.028","article-title":"A multi-frame image super-resolution method","volume":"90","author":"Li","year":"2010","journal-title":"Signal Process"},{"key":"ref_2","first-page":"657","article-title":"Understanding image fusion","volume":"70","author":"Zhang","year":"2004","journal-title":"Photogramm. 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