FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging


Mohammed Talha Alam (Mohamed bin Zayed University of Artificial Intelligence), Raza Imam (Mohamed bin Zayed University of Artificial Intelligence), Mohsen Guizani (Mohamed bin Zayed University of Artificial Intelligence), Fakhri Karray (University of Waterloo)
The 35th British Machine Vision Conference
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Abstract

The intersection of Astronomy and AI encounters significant challenges related to issues such as noisy backgrounds, lower resolution (LR), and the intricate process of filtering and archiving images from advanced telescopes like the James Webb. Given the dispersion of raw images in feature space, we have proposed a \textit{two-stage augmentation framework} entitled as \textbf{FLARE} based on \underline{f}eature \underline{l}earning and \underline{a}ugmented \underline{r}esolution \underline{e}nhancement. We first apply lower (LR) to higher resolution (HR) conversion followed by standard augmentations. Secondly, we integrate a diffusion approach to synthetically generate samples using class-concatenated prompts. By merging these two stages using weighted percentiles, we realign the feature space distribution, enabling a classification model to establish a distinct decision boundary and achieve superior generalization on various in-domain and out-of-domain tasks. We conducted experiments on several downstream cosmos datasets and on our optimally distributed \textbf{SpaceNet} dataset across 8-class fine-grained and 4-class macro classification tasks. FLARE attains the highest performance gain of 20.78\% for fine-grained tasks compared to similar baselines, while across different classification models, FLARE shows a consistent increment of an average of +15\%. This outcome underscores the effectiveness of the FLARE method in enhancing the precision of image classification, ultimately bolstering the reliability of astronomical research outcomes. Our code and SpaceNet dataset is available at \href{https://github.com/Razaimam45/PlanetX_Dxb}{\textit{https://github.com/Razaimam45/PlanetX\_Dxb}}.

Citation

@inproceedings{Alam_2024_BMVC,
author    = {Mohammed Talha Alam and Raza Imam and Mohsen Guizani and Fakhri Karray},
title     = {FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0505.pdf}
}


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