{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T01:33:46Z","timestamp":1776821626833,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3090303"],"award-info":[{"award-number":["2021YFC3090303"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFC3090304"],"award-info":[{"award-number":["2021YFC3090304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009108","name":"Shandong University","doi-asserted-by":"crossref","award":["52427901"],"award-info":[{"award-number":["52427901"]}],"id":[{"id":"10.13039\/100009108","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["42264008"],"award-info":[{"award-number":["42264008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Single-frequency ground penetrating radar (GPR) systems are fundamentally constrained by a trade-off between penetration depth and resolution, alongside issues like narrow bandwidth and ringing interference. To break this limitation, we have developed a multi-frequency data fusion technique grounded in convolutional sparse representation (CSR). The proposed methodology involves spatially registering multi-frequency GPR signals and fusing them via a CSR framework, where the convolutional dictionaries are derived from simulated high-definition GPR data. Extensive evaluation using information entropy, average gradient, mutual information, and visual information fidelity demonstrates the superiority of our method over traditional fusion approaches (e.g., weighted average, PCA, 2D wavelets). Tests on simulated and real data confirm that our CSR-based fusion successfully synergizes the deep penetration of low frequencies with the fine resolution of high frequencies, leading to substantial gains in GPR image clarity and interpretability.<\/jats:p>","DOI":"10.3390\/jimaging12010052","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T18:35:56Z","timestamp":1769193356000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-Frequency GPR Image Fusion Based on Convolutional Sparse Representation to Enhance Road Detection"],"prefix":"10.3390","volume":"12","author":[{"given":"Liang","family":"Fang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3319-3718","authenticated-orcid":false,"given":"Yuanjing","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junli","family":"Nie","sequence":"additional","affiliation":[{"name":"Guizhou University, Guiyang 550025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"ref_1","unstructured":"Yang, F., Zhang, Q., and Wang, P. 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