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Stokes physical constraint method for improving polarization imaging-based vision task.

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:00303992
DOI:10.1016/j.optlastec.2025.113408