DPGI: A Two-Stage Framework for Robust Super-Resolution Ghost Imaging in Noisy Environments.
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| Title: | DPGI: A Two-Stage Framework for Robust Super-Resolution Ghost Imaging in Noisy Environments. |
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| Authors: | Chen, Zhongyang1 czy112@xynu.edu.cn, Xuan, Hejun2 xuanhejun0896@xynu.edu.cn, Yang, Zhipeng1 yangzp@xynu.edu.cn |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2575-2582. 8p. |
| Subjects: | Image reconstruction, Mathematical regularization, Deep learning, Speckle interference, Robust statistics, High resolution imaging |
| Abstract: | Ghost imaging (GI) faces a fundamental tradeoff between acquisition speed and reconstruction fidelity, particularly under low sampling rates and high environmental noise. To address this, we propose Deep Processing Ghost Imaging (DPGI), a robust two-stage framework. The method first employs a Gaussian regularization step to transform chaotic speckle noise into a tractable structured blur, creating a clean prior. Subsequently, a Deep Plug-and-Play Super-Resolution (DPSR) model leverages this known degradation kernel to precisely restore high-frequency details. Comprehensive simulations demonstrate that DPGI consistently outperforms traditional GI, BM3D, and deep learning benchmarks. Specifically, under challenging conditions, DPGI achieves performance gains of up to 18.4% in PSNR and 45.1% in SSIM compared to end-to-end approaches, proving its superior robustness in noisy environments. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088887 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: DPGI: A Two-Stage Framework for Robust Super-Resolution Ghost Imaging in Noisy Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Zhongyang%22">Chen, Zhongyang</searchLink><relatesTo>1</relatesTo><i> czy112@xynu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xuan%2C+Hejun%22">Xuan, Hejun</searchLink><relatesTo>2</relatesTo><i> xuanhejun0896@xynu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Zhipeng%22">Yang, Zhipeng</searchLink><relatesTo>1</relatesTo><i> yangzp@xynu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2575-2582. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+regularization%22">Mathematical regularization</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Speckle+interference%22">Speckle interference</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Ghost imaging (GI) faces a fundamental tradeoff between acquisition speed and reconstruction fidelity, particularly under low sampling rates and high environmental noise. To address this, we propose Deep Processing Ghost Imaging (DPGI), a robust two-stage framework. The method first employs a Gaussian regularization step to transform chaotic speckle noise into a tractable structured blur, creating a clean prior. Subsequently, a Deep Plug-and-Play Super-Resolution (DPSR) model leverages this known degradation kernel to precisely restore high-frequency details. Comprehensive simulations demonstrate that DPGI consistently outperforms traditional GI, BM3D, and deep learning benchmarks. Specifically, under challenging conditions, DPGI achieves performance gains of up to 18.4% in PSNR and 45.1% in SSIM compared to end-to-end approaches, proving its superior robustness in noisy environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 2575 Subjects: – SubjectFull: Image reconstruction Type: general – SubjectFull: Mathematical regularization Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Speckle interference Type: general – SubjectFull: Robust statistics Type: general – SubjectFull: High resolution imaging Type: general Titles: – TitleFull: DPGI: A Two-Stage Framework for Robust Super-Resolution Ghost Imaging in Noisy Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Zhongyang – PersonEntity: Name: NameFull: Xuan, Hejun – PersonEntity: Name: NameFull: Yang, Zhipeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
| ResultId | 1 |
