https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/Head https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/assertion https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/provenance https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/pubinfo https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/assertion https://ieeexplore.ieee.org/document/10947128 http://purl.org/dc/terms/creator https://orcid.org/0000-0001-5740-8179 https://ieeexplore.ieee.org/document/10947128 http://purl.org/dc/terms/creator https://orcid.org/0000-0002-4135-7634 https://ieeexplore.ieee.org/document/10947128 http://purl.org/dc/terms/language https://www.omg.org/spec/LCC/Languages/LaISO639-1-LanguageCodes/en https://ieeexplore.ieee.org/document/10947128 http://purl.org/dc/terms/publisher https://ror.org/0078xmk34 https://ieeexplore.ieee.org/document/10947128 http://purl.org/dc/terms/subject http://aims.fao.org/aos/agrovoc/c_6498 https://ieeexplore.ieee.org/document/10947128 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fair/ff/terms/article https://ieeexplore.ieee.org/document/10947128 http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/fdof/ontology#FAIRDigitalObject https://ieeexplore.ieee.org/document/10947128 http://www.w3.org/2000/01/rdf-schema#comment Abstract: In remote sensing image processing for Earth and environmental applications, super-resolution (SR) is a crucial technique for enhancing the resolution of low-resolution (LR) images. 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. https://ieeexplore.ieee.org/document/10947128 https://schema.org/funder https://ror.org/0078xmk34 https://ieeexplore.ieee.org/document/10947128 https://w3id.org/fdof/ontology#hasMetadata https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s https://ieeexplore.ieee.org/document/10947128 https://www.w3.org/ns/dcat#contactPoint xzhou@mtech.edu https://ieeexplore.ieee.org/document/10947128 https://www.w3.org/ns/dcat#endDate 2024-04-01 https://ieeexplore.ieee.org/document/10947128 https://www.w3.org/ns/dcat#startDate 2023-08-01 https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/provenance https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/assertion http://www.w3.org/ns/prov#wasAttributedTo https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/pubinfo https://orcid.org/0009-0008-8411-2742 http://xmlns.com/foaf/0.1/name Emily Regalado https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://purl.org/dc/terms/created 2026-01-14T04:10:25.380Z https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://purl.org/dc/terms/creator https://orcid.org/0009-0008-8411-2742 https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://purl.org/dc/terms/license https://creativecommons.org/licenses/by/4.0/ https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://purl.org/nanopub/x/introduces https://ieeexplore.ieee.org/document/10947128 https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s http://purl.org/nanopub/x/wasCreatedAt https://nanodash.knowledgepixels.com/ https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate https://w3id.org/np/RArM5GTwgxg9qslGX-XiQ-KTTUwdoM0KB1YqmT4GqTizA https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/sig http://purl.org/nanopub/x/hasAlgorithm RSA https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/sig http://purl.org/nanopub/x/hasPublicKey MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/sig http://purl.org/nanopub/x/hasSignature eID7cTiQBhL+dn5cWs7I5nhXf2OGjnjYGj6VvY4GweTlR/mrpSWXxti6soDLEv7Zpr6d4mquroF4nGG3XqECKniC9X7R/WIqKlavvORJ8jaF8SvKeZ2lHSG/3nX8q3kV1jhi3ZsuydsaVYwOQOX0XecnJsT1LxTTO8yU3HPpVnMZwfsAS6kRG3Ae+FczNOQwyrjws3bZX/+EjBCDKdsPOyCEfiL/seEKzldUF/U5Hc2buJx3vwX8whbsGS27J3b/W0YaWQF81DD5iyWCtHplw+Oqj+bI66FmXdtMlubJOTQgOKGDndKEEMezqAbDdvPquUZ/YR2OKFYzaJyxQRt3hA== https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/sig http://purl.org/nanopub/x/hasSignatureTarget https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s/sig http://purl.org/nanopub/x/signedBy https://orcid.org/0009-0008-8411-2742