. . . . . . . . . . . "Abstract:\r\nIn 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." . . . "xzhou@mtech.edu" . "2024-04-01" . "2023-08-01" . . "Emily Regalado" . "2026-01-14T04:10:25.380Z"^^ . . . . . . . . . "RSA" . "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAxzr6UBGMW6c8tegz0babaledWUEQ0PLDE4tp7Iinbe2DZtAtY5JUptKYuStWDZx+QER4808P8dejNWRnBDzgthYJm/AyNSXflHSJhz2+NC+h7RylOLxbwLEQocmyKKiYxa2gT85m6ajVL2M6TnfG67nnK+K2f7iCGL6wYXRITD1q+7+5SWqBdDXIV921W4IKWaD2GJk+NRBoOqQhbsrk8Tn5XsNd7DMYVHk47oMDGbeBnrOIoRPsbBgAcoCsxxhiB9yN6Lf8EUbnlXVEDzJuZk048L1BDZL+6nkA8btTQGP2ijUFWA7rTrod3LjUDQWLZS95njjl867dtmv/znYkzwIDAQAB" . "eID7cTiQBhL+dn5cWs7I5nhXf2OGjnjYGj6VvY4GweTlR/mrpSWXxti6soDLEv7Zpr6d4mquroF4nGG3XqECKniC9X7R/WIqKlavvORJ8jaF8SvKeZ2lHSG/3nX8q3kV1jhi3ZsuydsaVYwOQOX0XecnJsT1LxTTO8yU3HPpVnMZwfsAS6kRG3Ae+FczNOQwyrjws3bZX/+EjBCDKdsPOyCEfiL/seEKzldUF/U5Hc2buJx3vwX8whbsGS27J3b/W0YaWQF81DD5iyWCtHplw+Oqj+bI66FmXdtMlubJOTQgOKGDndKEEMezqAbDdvPquUZ/YR2OKFYzaJyxQRt3hA==" . . .