Stokes physical constraint method for improving polarization imaging-based vision task.
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| Title: | Stokes physical constraint method for improving polarization imaging-based vision task. |
|---|---|
| Authors: | Song, Shaoting1 (AUTHOR), Mu, Tingkui1 (AUTHOR) tkmu@mail.xjtu.edu.cn |
| Source: | Optics & Laser Technology. Dec2025:Part A, Vol. 192, pN.PAG-N.PAG. 1p. |
| Subjects: | Stokes parameters, Optical polarization, Three-dimensional imaging, Image quality analysis, Data quality |
| Abstract: | • Proposes a physics-based approach to eliminate pixel-level error in polarization images. • Introduces a new metric to quantify Stokes vector consistency and assess polarization image quality. • Demonstrates significant improvements in the CLSV score and task performance after SPC processing. • Validates SPC method on advanced tasks like shape from polarization and polarization image fusion. Polarization vision captures polarization images to reveal scene properties inaccessible to conventional vision. However, the pixel-level error caused by sensor misalignment, ambient light leakage, and element imperfections often degrade the performance of polarization imaging-based vision tasks. To address this issue, we propose a novel Stokes Physical Constraints (SPC) method to model and mitigate the pixel-level error for improving vision tasks. Additionally, we introduce a new metric, the Constraint Level on Stokes Vector (CLSV) of each pixel, to quantify Stokes vector consistency and assess polarization image quality. Experiments on two publicly available datasets demonstrate the effectiveness of the SPC method and CLSV metric. The CLSV score increases from 38.24 % to 99.04 % in representative cases, indicating significant improvements in data quality. The SPC-processed data enhances the performance of polarization imaging-based vision tasks, including shape from polarization and polarization image fusion. In the 3D reconstruction task, neural networks trained on the SPC-processed data achieve better results in specular reflection and surface concavity-convexity. In the image fusion task, the SPC-processed data produces images with reduced artifacts, enhanced contrast, and improved visual quality, as confirmed by higher mutual information, spatial frequency, and visual fidelity scores. This work provides a robust framework for improving polarization imaging quality, with potential applications in biomedical imaging, remote sensing, and industrial inspection. [ABSTRACT FROM AUTHOR] |
| Copyright of Optics & Laser Technology is the property of Elsevier B.V. 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187265998 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Stokes physical constraint method for improving polarization imaging-based vision task. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Song%2C+Shaoting%22">Song, Shaoting</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mu%2C+Tingkui%22">Mu, Tingkui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tkmu@mail.xjtu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Optics+%26+Laser+Technology%22">Optics & Laser Technology</searchLink>. Dec2025:Part A, Vol. 192, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stokes+parameters%22">Stokes parameters</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+polarization%22">Optical polarization</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+imaging%22">Three-dimensional imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Image+quality+analysis%22">Image quality analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • Proposes a physics-based approach to eliminate pixel-level error in polarization images. • Introduces a new metric to quantify Stokes vector consistency and assess polarization image quality. • Demonstrates significant improvements in the CLSV score and task performance after SPC processing. • Validates SPC method on advanced tasks like shape from polarization and polarization image fusion. Polarization vision captures polarization images to reveal scene properties inaccessible to conventional vision. However, the pixel-level error caused by sensor misalignment, ambient light leakage, and element imperfections often degrade the performance of polarization imaging-based vision tasks. To address this issue, we propose a novel Stokes Physical Constraints (SPC) method to model and mitigate the pixel-level error for improving vision tasks. Additionally, we introduce a new metric, the Constraint Level on Stokes Vector (CLSV) of each pixel, to quantify Stokes vector consistency and assess polarization image quality. Experiments on two publicly available datasets demonstrate the effectiveness of the SPC method and CLSV metric. The CLSV score increases from 38.24 % to 99.04 % in representative cases, indicating significant improvements in data quality. The SPC-processed data enhances the performance of polarization imaging-based vision tasks, including shape from polarization and polarization image fusion. In the 3D reconstruction task, neural networks trained on the SPC-processed data achieve better results in specular reflection and surface concavity-convexity. In the image fusion task, the SPC-processed data produces images with reduced artifacts, enhanced contrast, and improved visual quality, as confirmed by higher mutual information, spatial frequency, and visual fidelity scores. This work provides a robust framework for improving polarization imaging quality, with potential applications in biomedical imaging, remote sensing, and industrial inspection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Optics & Laser Technology is the property of Elsevier B.V. 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: 10.1016/j.optlastec.2025.113408 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Stokes parameters Type: general – SubjectFull: Optical polarization Type: general – SubjectFull: Three-dimensional imaging Type: general – SubjectFull: Image quality analysis Type: general – SubjectFull: Data quality Type: general Titles: – TitleFull: Stokes physical constraint method for improving polarization imaging-based vision task. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Song, Shaoting – PersonEntity: Name: NameFull: Mu, Tingkui IsPartOfRelationships: – BibEntity: Dates: – D: 05 M: 12 Text: Dec2025:Part A Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00303992 Numbering: – Type: volume Value: 192 Titles: – TitleFull: Optics & Laser Technology Type: main |
| ResultId | 1 |
