Academic Journal
Millimeter-Wave Interferometric Synthetic Aperture Radiometer Imaging via Non-Local Similarity Learning.
| Τίτλος: | Millimeter-Wave Interferometric Synthetic Aperture Radiometer Imaging via Non-Local Similarity Learning. |
|---|---|
| Συγγραφείς: | Yang, Jin, Cao, Zhixiang, Li, Qingbo, Li, Yuehua |
| Πηγή: | Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3452, 20p |
| Θεματικοί όροι: | Millimeter waves, Image reconstruction, Imaging systems, Image reconstruction algorithms, Sampling theorem, Signal-to-noise ratio, Signal processing |
| Περίληψη: | In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Millimeter-Wave Interferometric Synthetic Aperture Radiometer Imaging via Non-Local Similarity Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Jin%22">Yang, Jin</searchLink><br /><searchLink fieldCode="AR" term="%22Cao%2C+Zhixiang%22">Cao, Zhixiang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Qingbo%22">Li, Qingbo</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yuehua%22">Li, Yuehua</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3452, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Millimeter+waves%22">Millimeter waves</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Imaging+systems%22">Imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction+algorithms%22">Image reconstruction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+theorem%22">Sampling theorem</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/electronics14173452 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 3452 Subjects: – SubjectFull: Millimeter waves Type: general – SubjectFull: Image reconstruction Type: general – SubjectFull: Imaging systems Type: general – SubjectFull: Image reconstruction algorithms Type: general – SubjectFull: Sampling theorem Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Signal processing Type: general Titles: – TitleFull: Millimeter-Wave Interferometric Synthetic Aperture Radiometer Imaging via Non-Local Similarity Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Jin – PersonEntity: Name: NameFull: Cao, Zhixiang – PersonEntity: Name: NameFull: Li, Qingbo – PersonEntity: Name: NameFull: Li, Yuehua IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 14 – Type: issue Value: 17 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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