Image recognition models have struggled to treat recognition robustness to real-world degradations. In this context, data augmentation methods like PixMix improve robustness but rely on generative arts and feature visualizations (FVis), which have copyright, drawing cost, and scalability issues. We propose MoireDB, a formula-generated interference-fringe image dataset for image augmentation enhancing robustness. MoireDB eliminates copyright concerns, reduces dataset assembly costs, and enhances robustness by leveraging illusory patterns. Experiments show that MoireDB augmented images outperforms traditional Fractal arts and FVis-based augmentations, making it a scalable and effective solution for improving model robustness against real-world degradations.
View on arXiv@article{matsuo2025_2502.01490,
title={ MoireDB: Formula-generated Interference-fringe Image Dataset },
author={ Yuto Matsuo and Ryo Hayamizu and Hirokatsu Kataoka and Akio Nakamura },
journal={arXiv preprint arXiv:2502.01490},
year={ 2025 }
}