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Directional Diffusion Probabilistic Modelling With Fuzzy Similarity Fields for Multipolarized Petrographic Registration.

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Title: Directional Diffusion Probabilistic Modelling With Fuzzy Similarity Fields for Multipolarized Petrographic Registration.
Authors: Wang, Wenqiang1 (AUTHOR), Chen, Bowei2 (AUTHOR) boweichen@xsyu.edu.cn, Yan, Bo1,3 (AUTHOR), Chen, Xin2 (AUTHOR), He, Wenmin1 (AUTHOR), Wang, Yongwei1 (AUTHOR), Peng, Lei1 (AUTHOR), Wang, Andong1 (AUTHOR), Bonopera, Marco (AUTHOR) marco.bonopera@unife.it
Source: Modelling & Simulation in Engineering. 6/7/2026, Vol. 2026, p1-13. 13p.
Subjects: Image registration, Fuzzy sets, Rock texture, Petrology
Abstract: Accurate registration of multipolarized petrographic thin‐section images is essential for quantitative characterization of rock microstructures under plane‐polarized and cross‐polarized illumination. However, conventional diffusion probabilistic models struggle with the pronounced uncertainty arising from birefringence, textural heterogeneity, illumination variations, and blurred mineral boundaries. To address this, we propose an intuitionistic fuzzy set–guided diffusion probabilistic model for robust registration of multipolarized thin‐section images. We derive an IFS‐based fuzzy similarity field to drive a directional diffusion process and enable semantically consistent propagation of correspondence probabilities. In parallel, a membership‐adaptive diffusion coefficient modulates the diffusion strength according to local correspondence confidence, enhancing propagation in reliable regions while suppressing it in uncertain areas, and an IFS‐weighted loss emphasizes high‐confidence pixels while penalizing high‐hesitancy regions to stabilize optimization in structurally ambiguous zones. Experiments on a benchmark dataset of 2634 rock thin‐section images spanning sedimentary, metamorphic, and igneous lithologies demonstrate that IFS‐DPM preserves mineral grain boundaries, fracture networks, and pore structures under complex textures and multipolarized conditions, providing a robust registration framework for petrographic analysis. [ABSTRACT FROM AUTHOR]
Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell 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.)
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  Data: Directional Diffusion Probabilistic Modelling With Fuzzy Similarity Fields for Multipolarized Petrographic Registration.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Wenqiang%22">Wang, Wenqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Bowei%22">Chen, Bowei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> boweichen@xsyu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Bo%22">Yan, Bo</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Xin%22">Chen, Xin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Wenmin%22">He, Wenmin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Yongwei%22">Wang, Yongwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peng%2C+Lei%22">Peng, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Andong%22">Wang, Andong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bonopera%2C+Marco%22">Bonopera, Marco</searchLink> (AUTHOR)<i> marco.bonopera@unife.it</i>
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  Data: <searchLink fieldCode="JN" term="%22Modelling+%26+Simulation+in+Engineering%22">Modelling & Simulation in Engineering</searchLink>. 6/7/2026, Vol. 2026, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Image+registration%22">Image registration</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Rock+texture%22">Rock texture</searchLink><br /><searchLink fieldCode="DE" term="%22Petrology%22">Petrology</searchLink>
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  Label: Abstract
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  Data: Accurate registration of multipolarized petrographic thin‐section images is essential for quantitative characterization of rock microstructures under plane‐polarized and cross‐polarized illumination. However, conventional diffusion probabilistic models struggle with the pronounced uncertainty arising from birefringence, textural heterogeneity, illumination variations, and blurred mineral boundaries. To address this, we propose an intuitionistic fuzzy set–guided diffusion probabilistic model for robust registration of multipolarized thin‐section images. We derive an IFS‐based fuzzy similarity field to drive a directional diffusion process and enable semantically consistent propagation of correspondence probabilities. In parallel, a membership‐adaptive diffusion coefficient modulates the diffusion strength according to local correspondence confidence, enhancing propagation in reliable regions while suppressing it in uncertain areas, and an IFS‐weighted loss emphasizes high‐confidence pixels while penalizing high‐hesitancy regions to stabilize optimization in structurally ambiguous zones. Experiments on a benchmark dataset of 2634 rock thin‐section images spanning sedimentary, metamorphic, and igneous lithologies demonstrate that IFS‐DPM preserves mineral grain boundaries, fracture networks, and pore structures under complex textures and multipolarized conditions, providing a robust registration framework for petrographic analysis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell 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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        Value: 10.1155/mse/2273342
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      – Code: eng
        Text: English
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        PageCount: 13
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      – SubjectFull: Image registration
        Type: general
      – SubjectFull: Fuzzy sets
        Type: general
      – SubjectFull: Rock texture
        Type: general
      – SubjectFull: Petrology
        Type: general
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      – TitleFull: Directional Diffusion Probabilistic Modelling With Fuzzy Similarity Fields for Multipolarized Petrographic Registration.
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            NameFull: Wang, Wenqiang
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            NameFull: Chen, Bowei
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              M: 06
              Text: 6/7/2026
              Type: published
              Y: 2026
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