Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry

TL;DR

This paper introduces a physics-based data augmentation framework for Mueller matrix polarimetry that preserves polarization properties, improving deep learning model performance on polarimetric datasets with limited samples.

Contribution

We developed a physically consistent augmentation method tailored for Mueller matrix images, addressing the limitations of standard transformations in polarimetric data.

Findings

Augmentations preserving polarization fidelity improve model accuracy.

Physics-based augmentations outperform conventional methods in polarimetric tasks.

Framework enhances deep learning robustness on limited polarimetric datasets.

Abstract

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.