RAaLXL-snB

https://w3id.org/np/RAaLXL-snBC1-5ideqKxbk9In0I-C2QwFh52x9pnfxX_s

 RAaLXL-snB FAIRDigitalObject article ▼ comment approve/disapprove edit as derived nanopublication create new with same template

https://ieeexplore.ieee.org/document/10947128

document/10947128

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https://ieeexplore.ieee.org/document/10947128

document/10947128

http://www.w3.org/2000/01/rdf-schema#comment has description (this is a literal)

"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."

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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article

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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0000-0001-5740-8179

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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"xzhou@mtech.edu"

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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en

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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Remote Sensing

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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0078xmk34

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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"2023-08-01"

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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"2024-04-01"

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https://ieeexplore.ieee.org/document/10947128

document/10947128

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0078xmk34

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Emily Regalado

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This is the identifier for this whole nanopublication. https://w3id.org/np/RAaLXL-snB... This nanopublication http://purl.org/dc/terms/creator is created by https://orcid.org/0009-0008-8411-2742

Emily Regalado

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Attribution 4.0 International (CC BY 4.0)

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https://orcid.org/0009-0008-8411-2742

Emily Regalado

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"Emily Regalado"

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"2026-01-14T04:10:25.380Z"

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RA7lSq6MuK

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document/10947128

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Emily Regalado

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This is the identifier for this whole nanopublication. https://w3id.org/np/RAaLXL-snB... This nanopublication http://purl.org/nanopub/x/wasCreatedAt was created at https://nanodash.knowledgepixels.com/

nanodash.knowledgepixels.com

.

Emily Regalado, 14 Jan 2026, 04:10:25 UTC

References