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 misleading 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.

翻译：穆勒矩阵偏振测量技术能够捕获偏振光与样本相互作用的关键信息，由于其独特的结构，在深度学习数据增强方面提出了特殊挑战。虽然数据增强是提升数据集多样性、减少过拟合的有效且经济的方法，但传统的旋转和翻转等变换无法保持穆勒矩阵图像中的偏振特性。为此，我们提出了一种通用模拟框架，该框架对穆勒矩阵施加物理一致的旋转和翻转操作，专门用于保持偏振保真度。我们在多个数据集上的实验结果表明，传统增强方法应用于偏振测量数据时可能导致误导性结果，这凸显了我们基于物理方法的必要性。实验中，我们首先将偏振专用增强方法与实际采集数据进行对比，以验证其物理一致性。随后将这些增强方法应用于语义分割任务，显著提升了模型的泛化能力和性能。本研究强调了在深度学习偏振成像中采用物理信息数据增强的必要性，为该领域更广泛的采用和更稳健的跨学科应用铺平了道路。特别值得注意的是，我们的框架为样本量有限的偏振测量数据集释放了深度学习模型的潜力。代码实现已发布于 github.com/hahnec/polar_augment。