Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916854717480960 |
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| author | Hahne, Christopher Rodriguez-Nunez, Omar Gros, Éléa Lucas, Théotim Hewer, Ekkehard Novikova, Tatiana Maragkou, Theoni Schucht, Philippe McKinley, Richard |
| author_facet | Hahne, Christopher Rodriguez-Nunez, Omar Gros, Éléa Lucas, Théotim Hewer, Ekkehard Novikova, Tatiana Maragkou, Theoni Schucht, Philippe McKinley, Richard |
| contents | Mueller matrix polarimetry captures essential information about polarized light interactions with a sample, presenting unique challenges for data augmentation in deep learning due to its distinct structure. While augmentations are an effective and affordable way to enhance dataset diversity and reduce overfitting, standard transformations like rotations and flips do not preserve the polarization properties in Mueller matrix images. To this end, we introduce a versatile simulation framework that applies physically consistent rotations and flips to Mueller matrices, tailored to maintain polarization fidelity. Our experimental results across multiple datasets reveal that conventional augmentations can lead to falsified results when applied to polarimetric data, underscoring the necessity of our physics-based approach. In our experiments, we first compare our polarization-specific augmentations against real-world captures to validate their physical consistency. We then apply these augmentations in a semantic segmentation task, achieving substantial improvements in model generalization and performance. This study underscores the necessity of physics-informed data augmentation for polarimetric imaging in deep learning (DL), paving the way for broader adoption and more robust applications across diverse research in the field. In particular, our framework unlocks the potential of DL models for polarimetric datasets with limited sample sizes. Our code implementation is available at github.com/hahnec/polar_augment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_07918 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry Hahne, Christopher Rodriguez-Nunez, Omar Gros, Éléa Lucas, Théotim Hewer, Ekkehard Novikova, Tatiana Maragkou, Theoni Schucht, Philippe McKinley, Richard Computer Vision and Pattern Recognition Medical Physics Mueller matrix polarimetry captures essential information about polarized light interactions with a sample, presenting unique challenges for data augmentation in deep learning due to its distinct structure. While augmentations are an effective and affordable way to enhance dataset diversity and reduce overfitting, standard transformations like rotations and flips do not preserve the polarization properties in Mueller matrix images. To this end, we introduce a versatile simulation framework that applies physically consistent rotations and flips to Mueller matrices, tailored to maintain polarization fidelity. Our experimental results across multiple datasets reveal that conventional augmentations can lead to falsified results when applied to polarimetric data, underscoring the necessity of our physics-based approach. In our experiments, we first compare our polarization-specific augmentations against real-world captures to validate their physical consistency. We then apply these augmentations in a semantic segmentation task, achieving substantial improvements in model generalization and performance. This study underscores the necessity of physics-informed data augmentation for polarimetric imaging in deep learning (DL), paving the way for broader adoption and more robust applications across diverse research in the field. In particular, our framework unlocks the potential of DL models for polarimetric datasets with limited sample sizes. Our code implementation is available at github.com/hahnec/polar_augment. |
| title | Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2411.07918 |