{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:25:24Z","timestamp":1760059524102,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Current depth map sensing technologies capture depth maps at low spatial resolution, rendering serious problems in various applications. In this paper, we propose a single depth map super-resolution method that combines the advantages of model-based methods and deep learning approaches. Specifically, we formulate a linear inverse problem which we solve by introducing a graph Laplacian regularizer. The regularization approach promotes smoothness and preserves the structural details of the observed depth map. We construct the graph Laplacian matrix by deploying latent features obtained from a pretrained deep learning model. The problem is solved with the Alternating Direction Method of Multipliers (ADMM). Experimental results show that the proposed approach outperforms existing optimization-based and deep learning solutions.<\/jats:p>","DOI":"10.3390\/info16060501","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T05:52:47Z","timestamp":1750139567000},"page":"501","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Graph Laplacian Regularizer from Deep Features for Depth Map Super-Resolution"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0972-4131","authenticated-orcid":false,"given":"George","family":"Gartzonikas","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2876-5788","authenticated-orcid":false,"given":"Evaggelia","family":"Tsiligianni","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9300-5860","authenticated-orcid":false,"given":"Nikos","family":"Deligiannis","sequence":"additional","affiliation":[{"name":"Department of Electronics and Informatics, Vrije Universiteit Brussel & IMEC, 1050 Brussels, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0678-4526","authenticated-orcid":false,"given":"Lisimachos P.","family":"Kondi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Ioannina, 451 10 Ioannina, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11654","DOI":"10.1109\/TITS.2021.3106055","article-title":"Self-supervised depth completion from direct visual-LiDAR odometry in autonomous driving","volume":"23","author":"Song","year":"2021","journal-title":"IEEE Trans. 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