# Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry

arXiv: https://arxiv.org/abs/2411.07918 · HTML: https://mlsanity.com/paper/2411.07918/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](https://mlsanity.com/paper/1712.04621/) (2017)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [Data augmentation for deep learning based accelerated MRI reconstruction with limited data](https://mlsanity.com/paper/2106.14947/) (2021)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)
- [PlenoptiCam v1.0: A light-field imaging framework](https://mlsanity.com/paper/2010.11687/) (2020)
- [Deep Demosaicing for Polarimetric Filter Array Cameras](https://mlsanity.com/paper/2211.13732/) (2022)
- [Polarimetric image augmentation](https://mlsanity.com/paper/2005.11044/) (2020)
- [Intra-operative Brain Tumor Detection with Deep Learning-Optimized Hyperspectral Imaging](https://mlsanity.com/paper/2302.02884/) (2023)
- [Physically-admissible polarimetric data augmentation for road-scene analysis](https://mlsanity.com/paper/2206.07431/) (2022)
- [MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution](https://mlsanity.com/paper/2410.06478/) (2024)

## Built on by (most cited first, 2 of 2)

- [MuellerPT: Decomposition Driven Pretraining for Dense Learning in Mueller Polarimetry](https://mlsanity.com/paper/2605.23840/) (2026)
- [Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging](https://mlsanity.com/paper/2607.25967/) (2026)

## Abstract (arXiv)

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.