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In this study, we proposed a novel algorithm of frequency-domain super-resolution with reconstruction from compressed representation. The algorithm follows a multistep procedure: first, an LR image in the space domain is transformed to the frequency domain using a Fourier transform. The frequency-domain representation is then expanded to the desired size (number of pixels) of a high-resolution (HR) image. This expanded frequency-domain image is subsequently inverse Fourier transformed back to the spatial domain, yielding an initial HR image. A final HR image is then reconstructed from the initial HR image using a low-rank regularization model that incorporates a nonlocal smoothed rank function (SRF). We evaluated the performance of the new algorithm by comparing the reconstructed HR images with those generated by several commonly used SR algorithms, including: 1) bicubic interpolation; 2) sparse representation; 3) adaptive sparse domain selection and adaptive regularization; 4) fuzzy-rule-based (FRB) algorithm; 5) SR convolutional neural networks (SRCNNs); 6) fast SR convolutional neural networks (FSRCNNs); 7) practical degradation model for deep blind image SR; 8) the frequency separation for real-world SR (FSSR); and 9) the enhanced SR generative adversarial networks (ESRGANs). The algorithms were tested on Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) multiresolution images over various locations, as well as on images with artificially added noise to assess the robustness of each algorithm. Results show that: 1) the proposed new algorithm outperforms the others in terms of the peak signal-to-noise ratio, structure similarity, and root-mean-square error and 2) it effectively suppresses noise during HR reconstruction from noisy low-resolution (LR) images, overcoming a key limitation of existing SR methods." } } } rows { prefix { value: "https://schema.org/" } } rows { name { value: "funder" } } rows { quad { p_iri { prefix_id: 7 } o_iri { prefix_id: 15 name_id: 19 } } } rows { name { value: "hasMetadata" } } rows { quad { p_iri { prefix_id: 6 name_id: 26 } o_iri { prefix_id: 1 name_id: 1 } } } rows { prefix { id: 10 value: "https://www.w3.org/ns/dcat#" } } rows { name { value: "contactPoint" } } rows { quad { p_iri { prefix_id: 10 name_id: 27 } o_literal { lex: "xzhou@mtech.edu" } } } rows { name { value: "endDate" } } rows { quad { p_iri { } o_literal { lex: "2024-04-01" } } } rows { name { value: "startDate" } } rows { quad { p_iri { } o_literal { lex: "2023-08-01" } } } rows { prefix { value: "http://www.w3.org/ns/prov#" } } rows { name { value: "wasAttributedTo" } } rows { name { value: "0009-0008-8411-2742" } } rows { quad { s_iri { prefix_id: 2 name_id: 4 } p_iri { prefix_id: 11 name_id: 30 } o_iri { prefix_id: 9 } g_iri { prefix_id: 2 name_id: 7 } } } rows { prefix { id: 3 value: "http://xmlns.com/foaf/0.1/" } } rows { name { value: "name" } } rows { quad { s_iri { prefix_id: 9 name_id: 31 } p_iri { prefix_id: 3 } o_literal { lex: "Emily Regalado" } g_iri { prefix_id: 2 name_id: 9 } } } rows { name { value: "created" } } rows { datatype { value: "http://www.w3.org/2001/XMLSchema#dateTime" } } rows { quad { s_iri { prefix_id: 1 name_id: 1 } p_iri { prefix_id: 4 name_id: 33 } o_literal { lex: "2026-01-14T04:10:25.380Z" datatype: 1 } } } rows { quad { p_iri { name_id: 13 } o_iri { prefix_id: 9 name_id: 31 } } } rows { name { value: "license" } } rows { prefix { id: 13 value: "https://creativecommons.org/licenses/by/4.0/" } } rows { quad { p_iri { prefix_id: 4 name_id: 34 } o_iri { prefix_id: 13 name_id: 2 } } } rows { prefix { value: "http://purl.org/nanopub/x/" } } rows { name { value: "introduces" } } rows { prefix { id: 16 value: "https://ieeexplore.ieee.org/document/" } } rows { quad { p_iri { prefix_id: 14 name_id: 35 } o_iri { prefix_id: 16 name_id: 12 } } } rows { name { value: "wasCreatedAt" } } rows { prefix { id: 12 value: "https://nanodash.knowledgepixels.com/" } } rows { quad { p_iri { prefix_id: 14 name_id: 36 } o_iri { prefix_id: 12 name_id: 2 } } } rows { prefix { id: 5 value: "https://w3id.org/np/o/ntemplate/" } } rows { name { value: "wasCreatedFromProvenanceTemplate" } } rows { name { value: "RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU" } } rows { quad { p_iri { prefix_id: 5 name_id: 37 } o_iri { prefix_id: 1 } } } rows { name { value: "wasCreatedFromPubinfoTemplate" } } rows { name { value: "RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw" } } rows { quad { p_iri { prefix_id: 5 } o_iri { prefix_id: 1 } } } rows { name { value: "RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI" } } rows { quad { o_iri { } } } rows { name { value: "wasCreatedFromTemplate" } } rows { name { value: "RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA" } } rows { quad { p_iri { prefix_id: 5 } o_iri { prefix_id: 1 } } } rows { name { value: "sig" } } rows { name { value: "hasAlgorithm" } } rows { quad { s_iri { prefix_id: 2 } p_iri { prefix_id: 14 } o_literal { lex: "RSA" } } } rows { name { value: "hasPublicKey" } } rows { quad { p_iri { } o_literal { lex: "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB" } } } rows { name { value: "hasSignature" } } rows { quad { p_iri { } o_literal { lex: "eID7cTiQBhL+dn5cWs7I5nhXf2OGjnjYGj6VvY4GweTlR/mrpSWXxti6soDLEv7Zpr6d4mquroF4nGG3XqECKniC9X7R/WIqKlavvORJ8jaF8SvKeZ2lHSG/3nX8q3kV1jhi3ZsuydsaVYwOQOX0XecnJsT1LxTTO8yU3HPpVnMZwfsAS6kRG3Ae+FczNOQwyrjws3bZX/+EjBCDKdsPOyCEfiL/seEKzldUF/U5Hc2buJx3vwX8whbsGS27J3b/W0YaWQF81DD5iyWCtHplw+Oqj+bI66FmXdtMlubJOTQgOKGDndKEEMezqAbDdvPquUZ/YR2OKFYzaJyxQRt3hA==" } } } rows { name { value: "hasSignatureTarget" } } rows { quad { p_iri { } o_iri { prefix_id: 1 name_id: 1 } } } rows { name { value: "signedBy" } } rows { quad { p_iri { prefix_id: 14 name_id: 49 } o_iri { prefix_id: 9 name_id: 31 } } }